BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the para...BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.展开更多
BACKGROUND Colorectal cancer(CRC)remains a major global health burden due to its high incidence and mortality,with treatment efficacy often hindered by tumor hetero-geneity,drug resistance,and a complex tumor microenv...BACKGROUND Colorectal cancer(CRC)remains a major global health burden due to its high incidence and mortality,with treatment efficacy often hindered by tumor hetero-geneity,drug resistance,and a complex tumor microenvironment(TME).Lactate metabolism plays a pivotal role in reshaping the TME,promoting immune eva-sion and epithelial-mesenchymal transition,making it a promising target for novel therapeutic strategies and prognostic modeling in CRC.AIM To offer an in-depth analysis of the role of lactate metabolism in CRC,high-lighting its significance in the TME and therapeutic response.METHODS Utilizing single-cell and transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas,we identified key lactate metabolic activities,particularly in the monocyte/macrophage subpopulation.RESULTS Seven lactate metabolism-associated genes were significantly linked to CRC prognosis and used to construct a predictive model.This model accurately forecasts patient outcomes and reveals notable distinct patterns of immune infiltration and transcriptomic profiles mutation profiles between high-and low-risk groups.High-risk patients demonstrated elevated immune cell infiltration,increased mutation frequencies,and heightened sensitivity to specific drugs(AZD6482,tozasertib,and SB216763),providing a foundation for personalized treatment approaches.Additionally,a nomogram integrating clinical and metabolic data effectively predicted 1-,3-,and 5-year survival rates.CONCLUSION This report underscored the pivotal mechanism of lactate metabolism in CRC prognosis and suggest novel avenues for therapeutic intervention.展开更多
BACKGROUND Partial hepatectomy continues to be the primary treatment approach for liver tumors,and post-hepatectomy liver failure(PHLF)remains the most critical lifethreatening complication following surgery.AIM To co...BACKGROUND Partial hepatectomy continues to be the primary treatment approach for liver tumors,and post-hepatectomy liver failure(PHLF)remains the most critical lifethreatening complication following surgery.AIM To comprehensively review the PHLF prognostic models developed in recent years and objectively assess the risk of bias in these models.METHODS This review followed the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline.Three databases were searched from November 2019 to December 2022,and references as well as cited literature in all included studies were manually screened in March 2023.Based on the defined inclusion criteria,articles on PHLF prognostic models were selected,and data from all included articles were extracted by two independent reviewers.The PROBAST was used to evaluate the quality of each included article.RESULTS A total of thirty-four studies met the eligibility criteria and were included in the analysis.Nearly all of the models(32/34,94.1%)were developed and validated exclusively using private data sources.Predictive variables were categorized into five distinct types,with the majority of studies(32/34,94.1%)utilizing multiple types of data.The area under the curve for the training models included ranged from 0.697 to 0.956.Analytical issues resulted in a high risk of bias across all studies included.CONCLUSION The validation performance of the existing models was substantially lower compared to the development models.All included studies were evaluated as having a high risk of bias,primarily due to issues within the analytical domain.The progression of modeling technology,particularly in artificial intelligence modeling,necessitates the use of suitable quality assessment tools.展开更多
Background:Lung cancer is the leading cause of cancer-related mortality,and while low-dose computed tomography screening may reduce mortality,emerging prognostic models show superior discriminative efficacy compared t...Background:Lung cancer is the leading cause of cancer-related mortality,and while low-dose computed tomography screening may reduce mortality,emerging prognostic models show superior discriminative efficacy compared to age-and smoking history-based screening.However,further research is needed to assess their reliability in predicting lung cancer risk in high-risk patients.Methods:This study evaluated the predictive performance and quality of existing lung cancer prognostic models through a systematic review and meta-analysis.A comprehensive search was conducted in PubMed,Cochrane,Web of Science,CNKI,and Wanfang for articles published between January 1,2000,and February 13,2025,identifying population-basedmodels incorporating all available modeling data.Results:Among 72 analyzed studies,models were developed from Asian(28 studies,including 23 Chinese cohorts)and European/American(48 studies)populations,with only 6 focusing on nonsmokers.Twenty-one models included genetic markers,15 used clinical factors,and 40 integrated epidemiological predictors.Although 37 models underwent external validation,only 4 demonstrated minimal bias and clinical applicability.A meta-analysis of 11 repeatedly validated models revealed calibration and discrimination,though some lacked calibration data.Conclusions:Few lung cancer prognostic models exist for nonsmokers.Most models exhibit poor predictive performance in external validations,with significant bias and limited application scope.Widespread external validation,standardized model development,and reporting techniques are needed to accurately identify high-risk individuals and ensure applicability across diverse populations.展开更多
BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,ofte...BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,often failing to capture the complexity of the disease.The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment.This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.AIM To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.METHODS This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2,PLAU,and CCNA2.The results were validated in an independent dataset.This study also examined the correlations between the model risk score and various clinical features,components of the immune microenvironment,chemotherapeutic drug sensitivity,and metabolism-related pathways.Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.RESULTS The prognostic model demonstrated significant predictive value,with the risk score showing a strong correlation with clinical features:It was significantly associated with tumor grade(G)(bP<0.01),moderately associated with tumor stage(T)(aP<0.05),and significantly correlated with residual tumor(R)status(bP<0.01).There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs.Furthermore,the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.CONCLUSION The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.展开更多
BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their assoc...BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.展开更多
BACKGROUND Breast cancer is a multifaceted and formidable disease with profound public health implications.Cell demise mechanisms play a pivotal role in breast cancer pathogenesis,with ATP-triggered cell death attract...BACKGROUND Breast cancer is a multifaceted and formidable disease with profound public health implications.Cell demise mechanisms play a pivotal role in breast cancer pathogenesis,with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic pertinence.AIM To investigate the impact of ATP-induced cell death(AICD)on breast cancer,enhancing our understanding of its mechanism.METHODS The foundational genes orchestrating AICD mechanisms were extracted from the literature,underpinning the establishment of a prognostic model.Simultaneously,a microRNA(miRNA)prognostic model was constructed that mirrored the gene-based prognostic model.Distinctions between high-and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized,with the aim of delineating common influence mechanisms,substantiated through enrichment analysis and immune infiltration assessment.RESULTS The mRNA prognostic model in this study encompassed four specific mRNAs:P2X purinoceptor 4,pannexin 1,caspase 7,and cyclin 2.The miRNA prognostic model integrated four pivotal miRNAs:hsa-miR-615-3p,hsa-miR-519b-3p,hsa-miR-342-3p,and hsa-miR-324-3p.B cells,CD4+T cells,CD8+T cells,endothelial cells,and macrophages exhibited inverse correlations with risk scores across all breast cancer subtypes.Furthermore,Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways,while miRNA risk scores significantly enriched 29 signaling pathways,with 16 pathways being jointly enriched.CONCLUSION Of paramount significance,distinct mRNA and miRNA signature models were devised tailored to AICD,both potentially autonomous prognostic factors.This study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools,offering an unparalleled window for innovative interventions.Essentially,this paper reveals the hitherto enigmatic link between AICD and breast cancer,potentially leading to revolutionary progress in personalized oncology.展开更多
BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for...BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis,thereby enhancing the ability to evaluate patient outcomes.AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis,thereby enhancing patient outcome assessment.METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM(type III),admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018.The dataset was divided into a development cohort and validation cohort in a ratio of 2:1.In the development cohort,we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients.Subsequently,we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis,calibration curves,and clinical decision curves.A nomogram was created to visually represent the prediction model,which was then externally validated using the validation cohort.RESULTS A total of 372 patients were included in this study,comprising 248 individuals in the development cohort and 124 individuals in the validation cohort.Based on Cox analysis results,our final prediction model incorporated five independent risk factors including albumin levels,primary tumor size,presence of extrahepatic metastases,surgical treatment status,and chemotherapy administration.The 1-,3-,and 5-years Area Under the Curve values in the development cohort are 0.753,0.859,and 0.909,respectively;whereas in the validation cohort,they are observed to be 0.772,0.848,and 0.923.Furthermore,the calibration curves demonstrated excellent consistency between observed values and actual values.Finally,the decision curve analysis curve indicated substantial net clinical benefit.CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model,demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.展开更多
Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production...Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC.展开更多
Objective:Hepatocellular carcinoma(HCC)presents substantial genetic and phenotypic diversity,making it challenging to predict patient outcomes.There is a clear need for novel biomarkers to better identify high-risk in...Objective:Hepatocellular carcinoma(HCC)presents substantial genetic and phenotypic diversity,making it challenging to predict patient outcomes.There is a clear need for novel biomarkers to better identify high-risk individuals.Long non-coding RNAs(lncRNAs)are known to play key roles in cell cycle regulation and genomic stability,and their dysregulation has been closely linked to HCC progression.Developing a prognostic model based on cell cycle-related lncRNAs could open up new possibilities for immunotherapy in HCC patients.Methods:Transcriptomic data and clinical samples were obtained from the TCGA-HCC dataset.Cell cycle-related gene sets were sourced from existing studies,and coexpression analysis identified relevant lncRNAs(correlation coefficient>0.4,P<0.001).Univariate analysis identified prognostic lncRNAs,which were then used in a LASSO regression model to create a risk score.This model was validated via cross-validation.HCC samples were classified on the basis of their risk scores.Correlations between the risk score and tumor mutational burden(TMB),tumor immune infiltration,immune checkpoint gene expression,and immunotherapy response were evaluated via R packages and various methods(TIMER,CIBERSORT,CIBERSORT-ABS,QUANTISEQ,MCP-COUNTER,XCELL,and EPIC).Results:Four cell cycle-related lncRNAs(AC009549.1,AC090018.2,PKD1P6-NPIPP1,and TMCC1-AS1)were significantly upregulated in HCC.These lncRNAs were used to create a risk score(risk score=0.492×AC009549.1+1.390×AC090018.2+1.622×PKD1P6-NPIPP1+0.858×TMCC1-AS1).This risk score had superior predictive value compared to traditional clinical factors(AUC=0.738).A nomogram was developed to illustrate the 1-year,3-year,and 5-year overall survival(OS)rates for individual HCC patients.Significant differences in TMB,immune response,immune cell infiltration,immune checkpoint gene expression,and drug responsiveness were observed between the high-risk and low-risk groups.Conclusion:The risk score model we developed enhances the prognostication of HCC patients by identifying those at high risk for poor outcomes.This model could lead to new immunotherapy strategies for HCC patients.展开更多
BACKGROUND: Chronic severe hepatitis is a serious illness with a high mortality rate. Discussion of prognostic judgment criteria for chronic severe hepatitis is of great value in clinical guidance. This study was desi...BACKGROUND: Chronic severe hepatitis is a serious illness with a high mortality rate. Discussion of prognostic judgment criteria for chronic severe hepatitis is of great value in clinical guidance. This study was designed to investigate the clinical and laboratory indices affecting the prognosis of chronic severe hepatitis and construct a prognostic model. METHODS: The clinical and laboratory indices of 213 patients with chronic severe hepatitis within 24 hours after diagnosis were analyzed retrospectively. Death or survival was limited to within 3 months after diagnosis. RESULTS: The mortality of all patients was 47.42%. Compared with the survival group, the age, basis of hepatocirrhosis, infection, degree of hepatic encephalopathy (HE) and the levels of total bilirubin (TBil), total cholesterol (CHO), cholinesterase (CHE), blood urea nitrogen (BUN), blood creatinine (Cr), blood sodium ion (Na), peripheral blood leukocytes (WBC), alpha-fetoprotein (AFP), international normalized ratio (INR) of blood coagulation and prothrombin time (PT) were significantly different in the group who died, but the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB) and hemoglobin (HGB) were not different between the two groups. At the same time, a regression model, Logit (P)=1.573xAge+1.338xHE-1.608xCHO+0.011xCr-0.109xNa+1.298xINR+11.057, was constructed by logistic regression analysis and the prognostic value of the model was higher than that of the MELD score. CONCLUSIONS: Multivariate analysis excels univariate anlysis in the prognosis of chronic severe hepatitis, and the regression model is of significant value in the prognosis of this disease.展开更多
BACKGROUND Nomograms for prognosis prediction in colorectal cancer patients are few,and prognostic indicators differ with age.AIM To construct a new nomogram survival prediction tool for middle-aged and elderly patien...BACKGROUND Nomograms for prognosis prediction in colorectal cancer patients are few,and prognostic indicators differ with age.AIM To construct a new nomogram survival prediction tool for middle-aged and elderly patients with stage III rectal adenocarcinoma.METHODS A total of 2773 eligible patients were divided into the training cohort(70%)and the validation cohort(30%).Optimal cutoff values were calculated using the X-tile software for continuous variables.Univariate and multivariate Cox proportional hazards regression analyses were used to determine overall survival(OS)and cancer-specific survival(CSS)-related prognostic factors.Two nomograms were successfully constructed.The discriminant and predictive ability and clinical usefulness of the model were also assessed by multiple methods of analysis.RESULTS The 95%CI in the training group was 0.719(0.690-0.749)and 0.733(0.702-0.74),while that in the validation group was 0.739(0.696-0.782)and 0.750(0.701-0.800)for the OS and CSS nomogram prediction models,respectively.In the validation group,the AUC of the three-year survival rate was 0.762 and 0.770,while the AUC of the five-year survival rate was 0.722 and 0.744 for the OS and CSS nomograms,respectively.The nomogram distinguishes all-cause mortality from cancer-specific mortality in patients with different risk grades.The time-dependent AUC and decision curve analysis showed that the nomogram had good clinical predictive ability and decision efficacy and was significantly better than the tumor-node-metastases staging system.CONCLUSION The survival prediction model constructed in this study is helpful in evaluating the prognosis of patients and can aid physicians in clinical diagnosis and treatment.展开更多
Background:Early singular nodular hepatocellular carcinoma(HCC)is an ideal surgical indication in clinical practice.However,almost half of the patients have tumor recurrence,and there is no reliable prognostic predict...Background:Early singular nodular hepatocellular carcinoma(HCC)is an ideal surgical indication in clinical practice.However,almost half of the patients have tumor recurrence,and there is no reliable prognostic prediction tool.Besides,it is unclear whether preoperative neoadjuvant therapy is necessary for patients with early singular nodular HCC and which patient needs it.It is critical to identify the patients with high risk of recurrence and to treat these patients preoperatively with neoadjuvant therapy and thus,to improve the outcomes of these patients.The present study aimed to develop two prognostic models to preoperatively predict the recurrence-free survival(RFS)and overall survival(OS)in patients with singular nodular HCC by integrating the clinical data and radiological features.Methods:We retrospective recruited 211 patients with singular nodular HCC from December 2009 to January 2019 at Eastern Hepatobiliary Surgery Hospital(EHBH).They all met the surgical indications and underwent radical resection.We randomly divided the patients into the training cohort(n=132)and the validation cohort(n=79).We established and validated multivariate Cox proportional hazard models by the preoperative clinicopathologic factors and radiological features for association with RFS and OS.By analyzing the receiver operating characteristic(ROC)curve,the discrimination accuracy of the models was compared with that of the traditional predictive models.Results:Our RFS model was based on HBV-DNA score,cirrhosis,tumor diameter and tumor capsule in imaging.RFS nomogram had fine calibration and discrimination capabilities,with a C-index of 0.74(95%CI:0.68-0.80).The OS nomogram,based on cirrhosis,tumor diameter and tumor capsule in imaging,had fine calibration and discrimination capabilities,with a C-index of 0.81(95%CI:0.74-0.87).The area under the receiver operating characteristic curve(AUC)of our model was larger than that of traditional liver cancer staging system,Korea model and Nomograms in Hepatectomy Patients with Hepatitis B VirusRelated Hepatocellular Carcinoma,indicating better discrimination capability.According to the models,we fitted the linear prediction equations.These results were validated in the validation cohort.Conclusions:Compared with previous radiography model,the new-developed predictive model was concise and applicable to predict the postoperative survival of patients with singular nodular HCC.Our models may preoperatively identify patients with high risk of recurrence.These patients may benefit from neoadjuvant therapy which may improve the patients’outcomes.展开更多
Objective To construct and verificate an RNA-binding protein(RBP)-associated prognostic model for gliomas using integrated bioinformatics analysis.Methods RNA-sequencing and clinic pathological data of glioma patients...Objective To construct and verificate an RNA-binding protein(RBP)-associated prognostic model for gliomas using integrated bioinformatics analysis.Methods RNA-sequencing and clinic pathological data of glioma patients from The Cancer Genome Atlas(TCGA)database and the Chinese Glioma Genome Atlas database(CGGA)were downloaded.The aberrantly expressed RBPs were investigated between gliomas and normal samples in TCGA database.We then identified prognosis related hub genes and constructed a prognostic model.This model was further validated in the CGGA-693 and CGGA-325 cohorts.Results Totally 174 differently expressed genes-encoded RBPs were identified,containing 85 down-regulated and 89 up-regulated genes.We identified five genes-encoded RBPs(ERI1,RPS2,BRCA1,NXT1,and TRIM21)as prognosis related key genes and constructed a prognostic model.Overall survival(OS)analysis revealed that the patients in the high-risk subgroup based on the model were worse than those in the low-risk subgroup.The area under the receiver operator characteristic curve(AUC)of the prognostic model was 0.836 in the TCGA dataset and 0.708 in the CGGA-693 dataset,demonstrating a favorable prognostic model.Survival analyses of the five RBPs in the CGGA-325 cohort validated the findings.A nomogram was constructed based on the five genes and validated in the TCGA cohort,confirming a promising discriminating ability for gliomas.Conclusion The prognostic model of the five RBPs might serve as an independent prognostic algorithm for gliomas.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is the most common type of primary liver cancer.With highly invasive biological characteristics and a lack of obvious clinical manifestations,HCC usually has a poor prognosis an...BACKGROUND Hepatocellular carcinoma(HCC)is the most common type of primary liver cancer.With highly invasive biological characteristics and a lack of obvious clinical manifestations,HCC usually has a poor prognosis and ranks fourth in cancer mortality.The aetiology and exact molecular mechanism of primary HCC are still unclear.AIM To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.METHODS By comparing the gene expression levels of patients with different cancer grades of HCC,we screened out differentially expressed genes associated with tumour grade.By protein-protein interaction(PPI)network analysis,we obtained the top 2 PPI networks and hub genes from these differentially expressed genes.By using least absolute shrinkage and selection operator Cox regression,13 prognostic genes were selected for feature extraction,and a prognostic risk model of HCC was established.RESULTS The model had significant prognostic ability in HCC.We also analysed the biological functions of these prognostic genes.CONCLUSION By comparing the gene profiles of patients with different stages of HCC,We have constructed a prognosis model consisting of 13 genes that have important prognostic value.This model has good application value and can be explained clinically.展开更多
Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glyc...Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.展开更多
BACKGROUND Accelerated therapeutic treatment should be considered in patients with progressive Crohn’s disease(CD)to prevent complications as well as surgery.Therefore,screening for risk factors and predicting the ne...BACKGROUND Accelerated therapeutic treatment should be considered in patients with progressive Crohn’s disease(CD)to prevent complications as well as surgery.Therefore,screening for risk factors and predicting the need for early surgery are of great importance in clinical practice.AIM To establish a model to predict CD-related early surgery.METHODS This was a retrospective study collecting data from CD patients diagnosed at our inflammatory bowel disease center from January 1,2012 to December 31,2016.All data were randomly stratified into a training set and a testing set at a ratio of 8:2.Multivariable logistic regression analysis was conducted with receiver operating characteristic curves constructed and areas under the curve calculated.This model was further validated with calibration and discrimination estimated.A nomogram was finally developed.RESULTS A total of 1002 eligible patients were enrolled with a mean follow-up period of 53.54±13.10 mo.In total,24.25%of patients received intestinal surgery within 1 year after diagnosis due to complications or disease relapse.Disease behavior(B2:OR[odds ratio]=6.693,P<0.001;B3:OR=14.405,P<0.001),smoking(OR=4.135,P<0.001),body mass index(OR=0.873,P<0.001)and C-reactive protein(OR=1.022,P=0.001)at diagnosis,previous perianal(OR=9.483,P<0.001)or intestinal surgery(OR=8.887,P<0.001),maximum bowel wall thickness(OR=1.965,P<0.001),use of biologics(OR=0.264,P<0.001),and exclusive enteral nutrition(OR=0.089,P<0.001)were identified as independent significant factors associated with early intestinal surgery.A prognostic model was established and further validated.The receiver operating characteristic curves and calculated areas under the curves(94.7%)confirmed an ideal predictive ability of this model with a sensitivity of 75.92%and specificity of 95.81%.A nomogram was developed to simplify the use of the predictive model in clinical practice.CONCLUSION This prognostic model can effectively predict 1-year risk of CD-related intestinal surgery,which will assist in screening progressive CD patients and tailoring therapeutic management.展开更多
Cuproptosis is a newly discovered form of apoptotic process that is thought to play an important role in cancer therapy.Long non-coding RNA(lncRNA)is involved in regulating many physiological and pathological activiti...Cuproptosis is a newly discovered form of apoptotic process that is thought to play an important role in cancer therapy.Long non-coding RNA(lncRNA)is involved in regulating many physiological and pathological activities of cells.The aim of this study was to investigate the prognostic significance of Cuproptosis-associated lncRNAs in osteosarcoma.Methods:The Gene expression profiling of osteosarcoma samples versus normal samples and corresponding clinical data were downloaded from the public databases UCSC Xena and GTEx,and the cuproptosis gene was obtained from the published literature,the prognostic model of osteosarcoma cuproptosis-related lncRNA was constructed by using coexpression network,minimum absolute contraction and selection algorithm(LASSO)and Cox regression model.Receiver operating characteristic(ROC)curves and nomograms were used to assess the predictive power of the model.Single-sample gene set enrichment analysis(ssGSEA)was used to explore the relationship between osteosarcoma immune cells and function in different risk groups.Results:181 cuproptosis-related lncRNAs were obtained by co-expression analysis of 19 cuproptosis genes collected.Ten lncRNAs were screened out by differential analysis and single-factor Cox analysis.Three cuproptosis-related lncrnas(AC124798.1,AC090152.1,AC090559.1)were screened by Lasso and multivariate Cox regression to construct the prognostic model.Patients were divided into high and low risk groups based on the median risk score.The results of overall survival,risk score distribution and survival status in the lowrisk group were better than those in the high-risk group,and were verified in the internal data.Univariate and multivariate Cox regression analyses showed that risk score was an independent prognostic factor.Nomograms and ROC curves showed that the prognostic model had good predictive ability.The results of ssGSEA suggest that immune cells and function may be inhibited in the high-risk group.Conclusion:The 3 cuproptosis-related lncRNAs may be helpful to guide the prognosis of osteosarcoma patients and provide some theoretical basis for clinical decision.展开更多
Objective:To identify the prognosis of hepatocellular carcinoma(HCC)and the effect of anti-cancer drug therapy by screening glutamine metabolism-related signature genes because glutamine metabolism plays an important ...Objective:To identify the prognosis of hepatocellular carcinoma(HCC)and the effect of anti-cancer drug therapy by screening glutamine metabolism-related signature genes because glutamine metabolism plays an important role in tumor development.Methods:We obtained gene expression samples of normal liver tissue and hepatocellular carcinoma from the TCGA database and GEO database,screened for differentially expressed glutamine metabolismrelated genes(GMRGs),constructed a prognostic model by lasso regression and step cox analysis,and assessed the differences in drug sensitivity between high-and low-risk groups.Results:We screened 23 differentially expressed GMRGs by differential analysis,and correlation loop plots and PPI protein interaction networks indicated that these differential genes were strongly correlated.The four most characterized genes(CAD,PPAT,PYCR3,and SLC7A11)were obtained by lasso regression and step cox,and a risk model was constructed and confirmed to have reliable predictive power in the TCGA dataset and GEO dataset.Finally,immunotherapy is better in the high-risk group than in the low-risk group,and chemotherapy and targeted drug therapy are better in the low-risk group than in the high-risk group.Conclusion:In conclusion,we have developed a reliable prognostic risk model characterized by glutamine metabolism-related genes,which may provide a viable basis for the prognosis and Treatment options of HCC patients.展开更多
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金Supported by Gansu Province Joint Fund General Program,No.24JRRA878Gansu Provincial Science and Technology Program Project,No.24JRRA1020+2 种基金Gansu Province Key Talent Program,No.2025RCXM006Teaching Research and Reform Program for Postgraduate Education at Gansu University of Traditional Chinese Medicine(GUSTCM),No.YBXM-202406Special Fund for Mentors of“Qihuang Talents”in the First-Level Discipline of Chinese Medicine,No.ZYXKBD-202415。
文摘BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.
基金Supported by Henan Province Science and Technology Research Project,No.232102310043Henan Provincial Science and Technology Research and Development Plan Joint Fund,No.222103810047Key Scientific Research Project Plan of Colleges and Universities in Henan Province,No.22A320033.
文摘BACKGROUND Colorectal cancer(CRC)remains a major global health burden due to its high incidence and mortality,with treatment efficacy often hindered by tumor hetero-geneity,drug resistance,and a complex tumor microenvironment(TME).Lactate metabolism plays a pivotal role in reshaping the TME,promoting immune eva-sion and epithelial-mesenchymal transition,making it a promising target for novel therapeutic strategies and prognostic modeling in CRC.AIM To offer an in-depth analysis of the role of lactate metabolism in CRC,high-lighting its significance in the TME and therapeutic response.METHODS Utilizing single-cell and transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas,we identified key lactate metabolic activities,particularly in the monocyte/macrophage subpopulation.RESULTS Seven lactate metabolism-associated genes were significantly linked to CRC prognosis and used to construct a predictive model.This model accurately forecasts patient outcomes and reveals notable distinct patterns of immune infiltration and transcriptomic profiles mutation profiles between high-and low-risk groups.High-risk patients demonstrated elevated immune cell infiltration,increased mutation frequencies,and heightened sensitivity to specific drugs(AZD6482,tozasertib,and SB216763),providing a foundation for personalized treatment approaches.Additionally,a nomogram integrating clinical and metabolic data effectively predicted 1-,3-,and 5-year survival rates.CONCLUSION This report underscored the pivotal mechanism of lactate metabolism in CRC prognosis and suggest novel avenues for therapeutic intervention.
基金Supported by The Science and Technology Innovation 2030-Major Project,No.2021ZD0140406.
文摘BACKGROUND Partial hepatectomy continues to be the primary treatment approach for liver tumors,and post-hepatectomy liver failure(PHLF)remains the most critical lifethreatening complication following surgery.AIM To comprehensively review the PHLF prognostic models developed in recent years and objectively assess the risk of bias in these models.METHODS This review followed the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline.Three databases were searched from November 2019 to December 2022,and references as well as cited literature in all included studies were manually screened in March 2023.Based on the defined inclusion criteria,articles on PHLF prognostic models were selected,and data from all included articles were extracted by two independent reviewers.The PROBAST was used to evaluate the quality of each included article.RESULTS A total of thirty-four studies met the eligibility criteria and were included in the analysis.Nearly all of the models(32/34,94.1%)were developed and validated exclusively using private data sources.Predictive variables were categorized into five distinct types,with the majority of studies(32/34,94.1%)utilizing multiple types of data.The area under the curve for the training models included ranged from 0.697 to 0.956.Analytical issues resulted in a high risk of bias across all studies included.CONCLUSION The validation performance of the existing models was substantially lower compared to the development models.All included studies were evaluated as having a high risk of bias,primarily due to issues within the analytical domain.The progression of modeling technology,particularly in artificial intelligence modeling,necessitates the use of suitable quality assessment tools.
基金funded by theGuangzhou Municipal Science and Tech-nology Bureau(No.2023A03J0507).
文摘Background:Lung cancer is the leading cause of cancer-related mortality,and while low-dose computed tomography screening may reduce mortality,emerging prognostic models show superior discriminative efficacy compared to age-and smoking history-based screening.However,further research is needed to assess their reliability in predicting lung cancer risk in high-risk patients.Methods:This study evaluated the predictive performance and quality of existing lung cancer prognostic models through a systematic review and meta-analysis.A comprehensive search was conducted in PubMed,Cochrane,Web of Science,CNKI,and Wanfang for articles published between January 1,2000,and February 13,2025,identifying population-basedmodels incorporating all available modeling data.Results:Among 72 analyzed studies,models were developed from Asian(28 studies,including 23 Chinese cohorts)and European/American(48 studies)populations,with only 6 focusing on nonsmokers.Twenty-one models included genetic markers,15 used clinical factors,and 40 integrated epidemiological predictors.Although 37 models underwent external validation,only 4 demonstrated minimal bias and clinical applicability.A meta-analysis of 11 repeatedly validated models revealed calibration and discrimination,though some lacked calibration data.Conclusions:Few lung cancer prognostic models exist for nonsmokers.Most models exhibit poor predictive performance in external validations,with significant bias and limited application scope.Widespread external validation,standardized model development,and reporting techniques are needed to accurately identify high-risk individuals and ensure applicability across diverse populations.
基金Supported by National Natural Science Foundation of China,No.82100581。
文摘BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,often failing to capture the complexity of the disease.The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment.This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.AIM To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.METHODS This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2,PLAU,and CCNA2.The results were validated in an independent dataset.This study also examined the correlations between the model risk score and various clinical features,components of the immune microenvironment,chemotherapeutic drug sensitivity,and metabolism-related pathways.Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.RESULTS The prognostic model demonstrated significant predictive value,with the risk score showing a strong correlation with clinical features:It was significantly associated with tumor grade(G)(bP<0.01),moderately associated with tumor stage(T)(aP<0.05),and significantly correlated with residual tumor(R)status(bP<0.01).There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs.Furthermore,the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.CONCLUSION The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.
基金Supported by the National Natural Science Foundation of China,No.81960100Applied Basic Foundation of Yunnan Province,No.202001AY070001-192+2 种基金Young and Middle-aged Academic and Technical Leaders Reserve Talents Program in Yunnan Province,No.202305AC160018Yunnan Revitalization Talent Support Program,No.RLQB20200004 and No.RLMY20220013and Yunnan Health Training Project of High-Level Talents,No.H-2017002。
文摘BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.
基金Supported by National Natural Science Foundation of China,No.81960877University Innovation Fund of Gansu Province,No.2021A-076+5 种基金Gansu Province Science and Technology Plan(Innovation Base and Talent Plan),No.21JR7RA561Natural Science Foundation of Gansu Province,No.21JR1RA267 and No.22JR5RA582Education Technology Innovation Project of Gansu Province,No.2022A-067Innovation Fund of Higher Education of Gansu Province,No.2023A-088Gansu Province Science and Technology Plan International Cooperation Field Project,No.23YFWA0005and Open Project of Key Laboratory of Dunhuang Medicine and Transformation of Ministry of Education,No.DHYX21-07,No.DHYX22-05,and No.DHYX21-01.
文摘BACKGROUND Breast cancer is a multifaceted and formidable disease with profound public health implications.Cell demise mechanisms play a pivotal role in breast cancer pathogenesis,with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic pertinence.AIM To investigate the impact of ATP-induced cell death(AICD)on breast cancer,enhancing our understanding of its mechanism.METHODS The foundational genes orchestrating AICD mechanisms were extracted from the literature,underpinning the establishment of a prognostic model.Simultaneously,a microRNA(miRNA)prognostic model was constructed that mirrored the gene-based prognostic model.Distinctions between high-and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized,with the aim of delineating common influence mechanisms,substantiated through enrichment analysis and immune infiltration assessment.RESULTS The mRNA prognostic model in this study encompassed four specific mRNAs:P2X purinoceptor 4,pannexin 1,caspase 7,and cyclin 2.The miRNA prognostic model integrated four pivotal miRNAs:hsa-miR-615-3p,hsa-miR-519b-3p,hsa-miR-342-3p,and hsa-miR-324-3p.B cells,CD4+T cells,CD8+T cells,endothelial cells,and macrophages exhibited inverse correlations with risk scores across all breast cancer subtypes.Furthermore,Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways,while miRNA risk scores significantly enriched 29 signaling pathways,with 16 pathways being jointly enriched.CONCLUSION Of paramount significance,distinct mRNA and miRNA signature models were devised tailored to AICD,both potentially autonomous prognostic factors.This study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools,offering an unparalleled window for innovative interventions.Essentially,this paper reveals the hitherto enigmatic link between AICD and breast cancer,potentially leading to revolutionary progress in personalized oncology.
文摘BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis,thereby enhancing the ability to evaluate patient outcomes.AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis,thereby enhancing patient outcome assessment.METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM(type III),admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018.The dataset was divided into a development cohort and validation cohort in a ratio of 2:1.In the development cohort,we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients.Subsequently,we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis,calibration curves,and clinical decision curves.A nomogram was created to visually represent the prediction model,which was then externally validated using the validation cohort.RESULTS A total of 372 patients were included in this study,comprising 248 individuals in the development cohort and 124 individuals in the validation cohort.Based on Cox analysis results,our final prediction model incorporated five independent risk factors including albumin levels,primary tumor size,presence of extrahepatic metastases,surgical treatment status,and chemotherapy administration.The 1-,3-,and 5-years Area Under the Curve values in the development cohort are 0.753,0.859,and 0.909,respectively;whereas in the validation cohort,they are observed to be 0.772,0.848,and 0.923.Furthermore,the calibration curves demonstrated excellent consistency between observed values and actual values.Finally,the decision curve analysis curve indicated substantial net clinical benefit.CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model,demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.
文摘Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC.
文摘Objective:Hepatocellular carcinoma(HCC)presents substantial genetic and phenotypic diversity,making it challenging to predict patient outcomes.There is a clear need for novel biomarkers to better identify high-risk individuals.Long non-coding RNAs(lncRNAs)are known to play key roles in cell cycle regulation and genomic stability,and their dysregulation has been closely linked to HCC progression.Developing a prognostic model based on cell cycle-related lncRNAs could open up new possibilities for immunotherapy in HCC patients.Methods:Transcriptomic data and clinical samples were obtained from the TCGA-HCC dataset.Cell cycle-related gene sets were sourced from existing studies,and coexpression analysis identified relevant lncRNAs(correlation coefficient>0.4,P<0.001).Univariate analysis identified prognostic lncRNAs,which were then used in a LASSO regression model to create a risk score.This model was validated via cross-validation.HCC samples were classified on the basis of their risk scores.Correlations between the risk score and tumor mutational burden(TMB),tumor immune infiltration,immune checkpoint gene expression,and immunotherapy response were evaluated via R packages and various methods(TIMER,CIBERSORT,CIBERSORT-ABS,QUANTISEQ,MCP-COUNTER,XCELL,and EPIC).Results:Four cell cycle-related lncRNAs(AC009549.1,AC090018.2,PKD1P6-NPIPP1,and TMCC1-AS1)were significantly upregulated in HCC.These lncRNAs were used to create a risk score(risk score=0.492×AC009549.1+1.390×AC090018.2+1.622×PKD1P6-NPIPP1+0.858×TMCC1-AS1).This risk score had superior predictive value compared to traditional clinical factors(AUC=0.738).A nomogram was developed to illustrate the 1-year,3-year,and 5-year overall survival(OS)rates for individual HCC patients.Significant differences in TMB,immune response,immune cell infiltration,immune checkpoint gene expression,and drug responsiveness were observed between the high-risk and low-risk groups.Conclusion:The risk score model we developed enhances the prognostication of HCC patients by identifying those at high risk for poor outcomes.This model could lead to new immunotherapy strategies for HCC patients.
文摘BACKGROUND: Chronic severe hepatitis is a serious illness with a high mortality rate. Discussion of prognostic judgment criteria for chronic severe hepatitis is of great value in clinical guidance. This study was designed to investigate the clinical and laboratory indices affecting the prognosis of chronic severe hepatitis and construct a prognostic model. METHODS: The clinical and laboratory indices of 213 patients with chronic severe hepatitis within 24 hours after diagnosis were analyzed retrospectively. Death or survival was limited to within 3 months after diagnosis. RESULTS: The mortality of all patients was 47.42%. Compared with the survival group, the age, basis of hepatocirrhosis, infection, degree of hepatic encephalopathy (HE) and the levels of total bilirubin (TBil), total cholesterol (CHO), cholinesterase (CHE), blood urea nitrogen (BUN), blood creatinine (Cr), blood sodium ion (Na), peripheral blood leukocytes (WBC), alpha-fetoprotein (AFP), international normalized ratio (INR) of blood coagulation and prothrombin time (PT) were significantly different in the group who died, but the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB) and hemoglobin (HGB) were not different between the two groups. At the same time, a regression model, Logit (P)=1.573xAge+1.338xHE-1.608xCHO+0.011xCr-0.109xNa+1.298xINR+11.057, was constructed by logistic regression analysis and the prognostic value of the model was higher than that of the MELD score. CONCLUSIONS: Multivariate analysis excels univariate anlysis in the prognosis of chronic severe hepatitis, and the regression model is of significant value in the prognosis of this disease.
基金The National Natural Science Foundation of China,No.81770631.
文摘BACKGROUND Nomograms for prognosis prediction in colorectal cancer patients are few,and prognostic indicators differ with age.AIM To construct a new nomogram survival prediction tool for middle-aged and elderly patients with stage III rectal adenocarcinoma.METHODS A total of 2773 eligible patients were divided into the training cohort(70%)and the validation cohort(30%).Optimal cutoff values were calculated using the X-tile software for continuous variables.Univariate and multivariate Cox proportional hazards regression analyses were used to determine overall survival(OS)and cancer-specific survival(CSS)-related prognostic factors.Two nomograms were successfully constructed.The discriminant and predictive ability and clinical usefulness of the model were also assessed by multiple methods of analysis.RESULTS The 95%CI in the training group was 0.719(0.690-0.749)and 0.733(0.702-0.74),while that in the validation group was 0.739(0.696-0.782)and 0.750(0.701-0.800)for the OS and CSS nomogram prediction models,respectively.In the validation group,the AUC of the three-year survival rate was 0.762 and 0.770,while the AUC of the five-year survival rate was 0.722 and 0.744 for the OS and CSS nomograms,respectively.The nomogram distinguishes all-cause mortality from cancer-specific mortality in patients with different risk grades.The time-dependent AUC and decision curve analysis showed that the nomogram had good clinical predictive ability and decision efficacy and was significantly better than the tumor-node-metastases staging system.CONCLUSION The survival prediction model constructed in this study is helpful in evaluating the prognosis of patients and can aid physicians in clinical diagnosis and treatment.
基金supported by grants from the Shanghai Rising-Star Program(19QA1408700)the National Natural Science Founda-tion of China(81972575 and 81521091)Clinical Research Plan of SHDC(SHDC2020CR5007)。
文摘Background:Early singular nodular hepatocellular carcinoma(HCC)is an ideal surgical indication in clinical practice.However,almost half of the patients have tumor recurrence,and there is no reliable prognostic prediction tool.Besides,it is unclear whether preoperative neoadjuvant therapy is necessary for patients with early singular nodular HCC and which patient needs it.It is critical to identify the patients with high risk of recurrence and to treat these patients preoperatively with neoadjuvant therapy and thus,to improve the outcomes of these patients.The present study aimed to develop two prognostic models to preoperatively predict the recurrence-free survival(RFS)and overall survival(OS)in patients with singular nodular HCC by integrating the clinical data and radiological features.Methods:We retrospective recruited 211 patients with singular nodular HCC from December 2009 to January 2019 at Eastern Hepatobiliary Surgery Hospital(EHBH).They all met the surgical indications and underwent radical resection.We randomly divided the patients into the training cohort(n=132)and the validation cohort(n=79).We established and validated multivariate Cox proportional hazard models by the preoperative clinicopathologic factors and radiological features for association with RFS and OS.By analyzing the receiver operating characteristic(ROC)curve,the discrimination accuracy of the models was compared with that of the traditional predictive models.Results:Our RFS model was based on HBV-DNA score,cirrhosis,tumor diameter and tumor capsule in imaging.RFS nomogram had fine calibration and discrimination capabilities,with a C-index of 0.74(95%CI:0.68-0.80).The OS nomogram,based on cirrhosis,tumor diameter and tumor capsule in imaging,had fine calibration and discrimination capabilities,with a C-index of 0.81(95%CI:0.74-0.87).The area under the receiver operating characteristic curve(AUC)of our model was larger than that of traditional liver cancer staging system,Korea model and Nomograms in Hepatectomy Patients with Hepatitis B VirusRelated Hepatocellular Carcinoma,indicating better discrimination capability.According to the models,we fitted the linear prediction equations.These results were validated in the validation cohort.Conclusions:Compared with previous radiography model,the new-developed predictive model was concise and applicable to predict the postoperative survival of patients with singular nodular HCC.Our models may preoperatively identify patients with high risk of recurrence.These patients may benefit from neoadjuvant therapy which may improve the patients’outcomes.
基金supported by the National Natural Science Foundation of China(No.82072795).
文摘Objective To construct and verificate an RNA-binding protein(RBP)-associated prognostic model for gliomas using integrated bioinformatics analysis.Methods RNA-sequencing and clinic pathological data of glioma patients from The Cancer Genome Atlas(TCGA)database and the Chinese Glioma Genome Atlas database(CGGA)were downloaded.The aberrantly expressed RBPs were investigated between gliomas and normal samples in TCGA database.We then identified prognosis related hub genes and constructed a prognostic model.This model was further validated in the CGGA-693 and CGGA-325 cohorts.Results Totally 174 differently expressed genes-encoded RBPs were identified,containing 85 down-regulated and 89 up-regulated genes.We identified five genes-encoded RBPs(ERI1,RPS2,BRCA1,NXT1,and TRIM21)as prognosis related key genes and constructed a prognostic model.Overall survival(OS)analysis revealed that the patients in the high-risk subgroup based on the model were worse than those in the low-risk subgroup.The area under the receiver operator characteristic curve(AUC)of the prognostic model was 0.836 in the TCGA dataset and 0.708 in the CGGA-693 dataset,demonstrating a favorable prognostic model.Survival analyses of the five RBPs in the CGGA-325 cohort validated the findings.A nomogram was constructed based on the five genes and validated in the TCGA cohort,confirming a promising discriminating ability for gliomas.Conclusion The prognostic model of the five RBPs might serve as an independent prognostic algorithm for gliomas.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is the most common type of primary liver cancer.With highly invasive biological characteristics and a lack of obvious clinical manifestations,HCC usually has a poor prognosis and ranks fourth in cancer mortality.The aetiology and exact molecular mechanism of primary HCC are still unclear.AIM To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.METHODS By comparing the gene expression levels of patients with different cancer grades of HCC,we screened out differentially expressed genes associated with tumour grade.By protein-protein interaction(PPI)network analysis,we obtained the top 2 PPI networks and hub genes from these differentially expressed genes.By using least absolute shrinkage and selection operator Cox regression,13 prognostic genes were selected for feature extraction,and a prognostic risk model of HCC was established.RESULTS The model had significant prognostic ability in HCC.We also analysed the biological functions of these prognostic genes.CONCLUSION By comparing the gene profiles of patients with different stages of HCC,We have constructed a prognosis model consisting of 13 genes that have important prognostic value.This model has good application value and can be explained clinically.
基金supported by the Public Health Research Project in Futian District,Shenzhen(Grant Nos.FTWS2020026,FTWS2021073).
文摘Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.
基金Supported by the National Natural Science Foundation of China,No.81900490,No.81670477,and No.81600419
文摘BACKGROUND Accelerated therapeutic treatment should be considered in patients with progressive Crohn’s disease(CD)to prevent complications as well as surgery.Therefore,screening for risk factors and predicting the need for early surgery are of great importance in clinical practice.AIM To establish a model to predict CD-related early surgery.METHODS This was a retrospective study collecting data from CD patients diagnosed at our inflammatory bowel disease center from January 1,2012 to December 31,2016.All data were randomly stratified into a training set and a testing set at a ratio of 8:2.Multivariable logistic regression analysis was conducted with receiver operating characteristic curves constructed and areas under the curve calculated.This model was further validated with calibration and discrimination estimated.A nomogram was finally developed.RESULTS A total of 1002 eligible patients were enrolled with a mean follow-up period of 53.54±13.10 mo.In total,24.25%of patients received intestinal surgery within 1 year after diagnosis due to complications or disease relapse.Disease behavior(B2:OR[odds ratio]=6.693,P<0.001;B3:OR=14.405,P<0.001),smoking(OR=4.135,P<0.001),body mass index(OR=0.873,P<0.001)and C-reactive protein(OR=1.022,P=0.001)at diagnosis,previous perianal(OR=9.483,P<0.001)or intestinal surgery(OR=8.887,P<0.001),maximum bowel wall thickness(OR=1.965,P<0.001),use of biologics(OR=0.264,P<0.001),and exclusive enteral nutrition(OR=0.089,P<0.001)were identified as independent significant factors associated with early intestinal surgery.A prognostic model was established and further validated.The receiver operating characteristic curves and calculated areas under the curves(94.7%)confirmed an ideal predictive ability of this model with a sensitivity of 75.92%and specificity of 95.81%.A nomogram was developed to simplify the use of the predictive model in clinical practice.CONCLUSION This prognostic model can effectively predict 1-year risk of CD-related intestinal surgery,which will assist in screening progressive CD patients and tailoring therapeutic management.
基金National Natural Science Foundation Project of China (No.81860793)Natural Science Foundation Project of Guangxi Province (No.2020JJA140375)Guangxi Graduate Education Innovation Program (No.YCSY2022027)。
文摘Cuproptosis is a newly discovered form of apoptotic process that is thought to play an important role in cancer therapy.Long non-coding RNA(lncRNA)is involved in regulating many physiological and pathological activities of cells.The aim of this study was to investigate the prognostic significance of Cuproptosis-associated lncRNAs in osteosarcoma.Methods:The Gene expression profiling of osteosarcoma samples versus normal samples and corresponding clinical data were downloaded from the public databases UCSC Xena and GTEx,and the cuproptosis gene was obtained from the published literature,the prognostic model of osteosarcoma cuproptosis-related lncRNA was constructed by using coexpression network,minimum absolute contraction and selection algorithm(LASSO)and Cox regression model.Receiver operating characteristic(ROC)curves and nomograms were used to assess the predictive power of the model.Single-sample gene set enrichment analysis(ssGSEA)was used to explore the relationship between osteosarcoma immune cells and function in different risk groups.Results:181 cuproptosis-related lncRNAs were obtained by co-expression analysis of 19 cuproptosis genes collected.Ten lncRNAs were screened out by differential analysis and single-factor Cox analysis.Three cuproptosis-related lncrnas(AC124798.1,AC090152.1,AC090559.1)were screened by Lasso and multivariate Cox regression to construct the prognostic model.Patients were divided into high and low risk groups based on the median risk score.The results of overall survival,risk score distribution and survival status in the lowrisk group were better than those in the high-risk group,and were verified in the internal data.Univariate and multivariate Cox regression analyses showed that risk score was an independent prognostic factor.Nomograms and ROC curves showed that the prognostic model had good predictive ability.The results of ssGSEA suggest that immune cells and function may be inhibited in the high-risk group.Conclusion:The 3 cuproptosis-related lncRNAs may be helpful to guide the prognosis of osteosarcoma patients and provide some theoretical basis for clinical decision.
基金Key Project of Natural Science Research in Anhui Universities (No.KJ2021A0774)National Student Innovation and Entrepreneurship Training Program Grant (No.202110367037)。
文摘Objective:To identify the prognosis of hepatocellular carcinoma(HCC)and the effect of anti-cancer drug therapy by screening glutamine metabolism-related signature genes because glutamine metabolism plays an important role in tumor development.Methods:We obtained gene expression samples of normal liver tissue and hepatocellular carcinoma from the TCGA database and GEO database,screened for differentially expressed glutamine metabolismrelated genes(GMRGs),constructed a prognostic model by lasso regression and step cox analysis,and assessed the differences in drug sensitivity between high-and low-risk groups.Results:We screened 23 differentially expressed GMRGs by differential analysis,and correlation loop plots and PPI protein interaction networks indicated that these differential genes were strongly correlated.The four most characterized genes(CAD,PPAT,PYCR3,and SLC7A11)were obtained by lasso regression and step cox,and a risk model was constructed and confirmed to have reliable predictive power in the TCGA dataset and GEO dataset.Finally,immunotherapy is better in the high-risk group than in the low-risk group,and chemotherapy and targeted drug therapy are better in the low-risk group than in the high-risk group.Conclusion:In conclusion,we have developed a reliable prognostic risk model characterized by glutamine metabolism-related genes,which may provide a viable basis for the prognosis and Treatment options of HCC patients.
