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: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
Adult T-cell lymphoblastic lymphoma(T-LBL)is a rare and aggressive subtype of non-Hodgkin’s lymphoma that differs from pediatric T-LBL and has a worse prognosis.Due to its rarity,little is known about the genetic and...Adult T-cell lymphoblastic lymphoma(T-LBL)is a rare and aggressive subtype of non-Hodgkin’s lymphoma that differs from pediatric T-LBL and has a worse prognosis.Due to its rarity,little is known about the genetic and molecular characteristics,optimal treatment modalities,and prognostic factors of adult T-LBL.Therefore,we summarized the existing studies to comprehensively discuss the above issues in this review.Genetic mutations of NOTCH1/FBXW7,PTEN,RAS,and KMT2D,together with abnormal activation of signaling pathways,such as the JAK-STAT signaling pathway were described.We also discussed the therapeutic modalities.Once diagnosed,adult T-LBL patients should receive intensive or pediatric acute lymphoblastic leukemia regimen and central nervous system prophylaxis as soon as possible,and cranial radiation-free protocols are appropriate.Mediastinal radiotherapy improves clinical outcomes,but adverse events are of concern.Hematopoietic stem cell transplantation may be considered for adult T-LBL patients with high-risk factors or those with relapsed/refractory disease.Besides,several novel prognostic models have been constructed,such as the 5-miRNAs-based classifier,11-gene-based classifier,and 4-CpG-based classifier,which have presented significant prognostic value in adult T-LBL.展开更多
This editorial narrative review discussed Budd-Chiari syndrome(BCS),which re-presents a rare but critical vascular liver disease resulting in an obstruction of he-patic venous outflow.Despite having a unifying mechani...This editorial narrative review discussed Budd-Chiari syndrome(BCS),which re-presents a rare but critical vascular liver disease resulting in an obstruction of he-patic venous outflow.Despite having a unifying mechanism,the syndrome shows a large heterogeneity across presentation,cause,and disease trajectory,compli-cating diagnosis and management.Based on established prognostic scoring systems,the New Clichy Score,the BCS-transjugular intrahepatic portosystemic shunt Index,the Zeitoun Score,and the Pediatric End-stage Liver Disease score were examined.These scoring systems are used for risk stratification and thera-peutic decision-making.Although these models deliver suitability information,their static parameters,narrow validation,and limited generalizability reduce their usefulness in diverse populations.Specific challenges are highlighted in pediatric patients,pregnant females,and individuals with myeloproliferative neoplasms for whom current tools often fall short.Moreover,there remains uncertainty regarding the durability of Pediatric End-stage Liver Disease score response and longer-term risks,such as hepatocellular carcinoma.There is a need to have a dynamic prognostic model that uses imaging and genetic factors in future studies.The article discussed enhancing recruitment to improve research.Overall,this article provided a contemporary,evidence-based approach for cli-nicians to aid in the evaluation and treatment of BCS.展开更多
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 Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to dev...BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.展开更多
Ovarian cancer has one of the highest mortality rates among gynecological malignancies.This disease has a low early detection rate,a high postoperative recurrence rate,and a 5-year survival rate of only 40%.Hence,ther...Ovarian cancer has one of the highest mortality rates among gynecological malignancies.This disease has a low early detection rate,a high postoperative recurrence rate,and a 5-year survival rate of only 40%.Hence,there is an urgent need to improve the early diagnosis and prognosis of ovarian cancer.Prediction models can effectively estimate the risk of disease occurrence,as well as its prognosis.Recently,many studies have established multiple ovarian cancer prediction models based on different regions and populations.These models can improve the detection rate and optimize the prognosis management to a certain extent.Herein,the construction principle of the ovarian cancer risk prediction model and its validation are summarized;furthermore,comprehensive reviews and comparisons of the different types of these models are made.Therefore,our review may be of great significance for the whole course of ovarian cancer management.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 Gastric signet-ring cell carcinoma(GSRCC)is a more aggressive subtype of gastric cancer compared to gastric adenocarcinoma(GA),with an increasing incidence.However,the prognostic differences between these s...BACKGROUND Gastric signet-ring cell carcinoma(GSRCC)is a more aggressive subtype of gastric cancer compared to gastric adenocarcinoma(GA),with an increasing incidence.However,the prognostic differences between these subtypes,particularly in re-sectable cases,remain unclear.AIM To evaluate prognostic factors and develop a predictive model for GA and GSRCC patients undergoing curative resection.METHODS This retrospective cohort study included patients with GA and GSRCC who underwent curative surgery at the National Cancer Center/Cancer Hospital,Chinese Academy of Medical Sciences,from 2011 to 2018.Propensity score ma-tching(PSM)(1:1)balanced the baseline characteristics.Prognostic factors were identified using univariate and multivariate Cox and least absolute shrinkage and selection operator(LASSO)regression analyses.Model performance was eva-luated through calibration curves,decision curve analysis(DCA),and time-dependent receiver operating characteristic curves.Subgroup analysis and Ka-plan-Meier survival curves were generated.RESULTS In a cohort of 3027 patients,the GSRCC group was characterized by a significantly higher prevalence of individuals under 60 years of age,females,cases with poor differentiation,and early-stage(stage I)disease(all P<0.001).After PSM,the baseline was balanced and 761 patients were retained in each group.Variables identified through univariate Cox regression were included in the LASSO regression analysis.Mul-tivariate Cox regression analysis identified age,tumor differentiation,tumor size,vascular invasion,and post-treatment nodal margin staging as independent prognostic factors.Subgroup analysis indicated a notably poorer prognosis for GSRCC in patients aged 60 and above(hazard ratio=1.36,P=0.025).The nomogram(C-index=0.755)exhibited greater predictive accuracy than tumor node metastasis(TNM)staging for 1-,3-,and 5-year overall survival(all P<0.001),and provided a higher clinical net benefit according to DCA.CONCLUSION This study systematically compared resectable GA and GSRCC,revealing no overall survival difference.However,GSRCC demonstrated a significantly elevated mortality risk in subgroups stratified by age and tumor size.Multivariate analysis identified age,differentiation,tumor size,vascular invasion,and TNM stage as independent prognostic factors.The nomogram integrates clinicopathological features for precise risk stratification,surpassing traditional TNM staging.展开更多
Background:The centrosome,a crucial cellular structure involved in the mitotic process of eukaryotic cells,plays a significant role in tumor progression by regulating the growth and differentiation of neoplastic cells...Background:The centrosome,a crucial cellular structure involved in the mitotic process of eukaryotic cells,plays a significant role in tumor progression by regulating the growth and differentiation of neoplastic cells.This makes the centrosome a promising target for therapeutic strategies in cancer treatment.Methods:Utilizing data from the TCGA database,we identified centrosome-related genes and constructed a prognostic model for 518 lung adenocarcinoma patients.Prognosis-associated genes were initially screened using univariate Cox regression,with overfitting minimized by applying LASSO regression to remove collinearity.Finally,a set of 12 genes was selected through multivariable Cox regression for inclusion in the prognostic model.Results:The model’s performance was assessed using ROC curve analysis,demonstrating a robust predictive ability with an AUC of 0.728 in the training group and 0.695 in the validation group.Differential expression analysis between high-risk(HRLAs)and low-risk(LRLAs)individuals was performed,followed by enrichment analyses using KEGG,GO,Progeny,GSVA,and GSEA.These analyses revealed significant differences in immune-related pathways between the two groups.Immune microenvironment assessment through ssGSEA and ESTIMATE indicated that individuals with poor prognosis exhibited lower immune,stromal,and ESTIMATE scores,along with higher tumor purity,suggesting an impaired immune microenvironment in HRLAs patients.Drug susceptibility analysis and molecular docking showed that HRLAs individuals were more responsive to docetaxel,emphasizing the therapeutic relevance of paclitaxel in this cohort.Conclusion:We successfully developed and validated a centrosome-associated gene-based prognostic model,offering clinicians valuable insights for improved decision-making and personalized treatment strategies.This model may facilitate the identification of high-risk patients and guide therapeutic interventions in lung adenocarcinoma.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is a significant global health challenge with rising incidence rates and poor prognoses.Aminoacyl-tRNA synthetases(ARSs)are important regulators implicated in the occurrence and...BACKGROUND Hepatocellular carcinoma(HCC)is a significant global health challenge with rising incidence rates and poor prognoses.Aminoacyl-tRNA synthetases(ARSs)are important regulators implicated in the occurrence and progression of several cancers.However,their specific function in HCC remains unclear,and ARSs-related prognostic factors for patient stratification are lacking.AIM To investigate the ARSs-related mechanisms of HCC and establish an effective prognostic risk model for stratifying patients with HCC.METHODS We screened ARSs genes of interest using differential gene expression,mutation,and survival analysis.Western blot and Immunohistochemistry were used to analyze MARS1 expression in the liver tissues of patients with HCC.Functional studies,including CCK-8 cell viability assay,EdU cell proliferation assay,cell cycle assays,Transwell migration and invasion assays,and in vivo tumor xenograft models,were conducted to comprehensively elucidate the specific role of MARS1 in HCC.Moreover,the MARS1-related prognostic score(MRPS)was established by LASSO regression and Cox regression analysis in The Cancer Genome Atlas-HCC and GSE14520 cohorts.Patients’immunotherapy and chemotherapy responses were predicted by immunomicroenvironment and drug susceptibility analysis in both subgroups.RESULTS MARS1 was selected as a target gene from a series of ARSs genes,with markedly higher expression observed in HCC tissues compared to adjacent non-cancerous tissues.Silencing MARS1 considerably impeded the proliferation,migration,invasion,and tumorigenic abilities of HCC cells in vitro and in vivo.Moreover,high MRPSs were associated with poor overall survival,altered infiltration of T cells,macrophages,monocytes,elevated immune checkpoint expression(PD-L1,CTLA4,LAG3),and reduced drug sensitivity in HCC.CONCLUSION MARS1 promotes HCC development and represents a potential therapeutic target for HCC management.Furthermore,MRPS serves as an independent prognostic factor for survival and a predictor of tumor treatment response.展开更多
Background:Primary liver cancer poses a significant global health burden,with projections indicating a surpassing of one million cases by 2025.Cuproptosis,a copper-dependent mechanism of cell death,plays a crucial rol...Background:Primary liver cancer poses a significant global health burden,with projections indicating a surpassing of one million cases by 2025.Cuproptosis,a copper-dependent mechanism of cell death,plays a crucial role in the pathogenesis,progression,and prognosis of various cancers,including hepatocellular carcinoma(HCC).Purpose:This study aimed to develop a prognostic model for HCC based on cuproptosis-related genes,utilizing clinical data and gene expression profiles from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases.Materials and Methods:Clinical features and gene expression data of HCC patients were collected from publicly available databases.Patients from TCGA were randomly divided into training and testing sets,and Lasso Cox regression was applied to develop a predictive model using cuproptosis-related genes.Results:The analysis identified Copper Metabolism Domain Containing 1(COMMD1)as a potential prognostic marker for HCC,with deletion of this gene impacting disease progression.Cellular functional experiments validated the role of COMMD1 in HCC.Conclusions:COMMD1 emerges as a promising candidate for HCC treatment,with implications for prognosis prediction and therapeutic targeting.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 Gastric cancer,a globally prevalent malignant tumor,continues to exhibit high incidence and mortality rates.Although radical gastrectomy remains the primary treatment for this disease,postoperative complica...BACKGROUND Gastric cancer,a globally prevalent malignant tumor,continues to exhibit high incidence and mortality rates.Although radical gastrectomy remains the primary treatment for this disease,postoperative complications frequently arise,negatively impacting short-term recovery and significantly reducing patients’quality of life.In this context,accurately predicting the risk of postoperative recurrence and metastasis,coupled with targeted interventions,could substantially improve patient outcomes.The C-reactive protein-triglyceride-glucose index(CTI),a composite biomarker that integrates metabolic disturbances and systemic inflammation,has garnered increasing attention in oncology.The prognostic nutritional index(PNI),a composite measure based on serum albumin and peripheral blood lymphocyte count,is used to evaluate both the nutritional status and systemic immune function of patients.In recent years,both the CTI and PNI have demonstrated significant prognostic value in predicting tumor outcomes,assessing treatment responses,and formulating personalized treatment strategies.AIM To investigate whether the combined inflammation and insulin resistance marker,the CTI,can serve as a prognostic indicator for patients undergoing radical gastrectomy for gastric cancer.Additionally,it seeks to develop a predictive model by incorporating the PNI alongside CTI.METHODS This retrospective study included a total of 300 patients who underwent radical gastrectomy.The patients were classified into high and low CTI groups based on their CTI index.Cox proportional hazards regression analysis was performed to identify independent prognostic factors influencing overall survival(OS)and disease-free survival(DFS),and two nomogram models were developed.RESULTS Of the included patients,131 had a high CTI and 169 had a low CTI.The DFS period of the low CTI group was significantly longer than that of the high CTI group.The number of postoperative adjuvant treatments,T stage,N stage,CTI,and PNI were identified as independent prognostic factors for DFS.The hazard ratio for CTI was 2.07(95%confidence interval:1.36-3.17,P<0.001).In terms of OS,the OS period of the low CTI group was significantly longer than that of the high CTI group.Whether adjuvant treatment was administered,T stage,CTI,and PNI were independent prognostic factors for OS.The hazard ratio for CTI was 2.47(95%confidence interval:1.44-4.23,P=0.001).The nomogram models for OS and DFS further emphasized the importance of CTI as a key predictor of patient prognosis.CONCLUSION CTI is a long-term prognostic indicator for the outcome of radical gastrectomy for gastric cancer.Patients with lower CTI values have a better prognosis.The prediction models constructed by combining CTI with PNI has good predictive ability for DFS and OS after radical gastrectomy.展开更多
Objectives:Triple-negative breast cancer(TNBC)presents a major treatment challenge due to its aggressive behavior.The dysfunction of the Golgi apparatus(GA)contributes to the development of various cancers.This study ...Objectives:Triple-negative breast cancer(TNBC)presents a major treatment challenge due to its aggressive behavior.The dysfunction of the Golgi apparatus(GA)contributes to the development of various cancers.This study aimed to utilize GA-related genes(GARGs)to forecast the prognosis and immune profile of TNBC.Methods:The data were downloaded from The Cancer Genome Atlas(TCGA)database,including 175 TNBC and 99 healthy samples.The differentially expressed GARGs(DEGARGs)were analyzed using the TCGA biolinks package.The patients with TNBC were classified into two clusters utilizing the ConsensusClusterPlus package according to prognosis-related DEGARGs,followed by comparing the differences in prognosis and immune infiltration between the two clusters.Next,LASSO and stepwise Cox regression were applied to establish a GARGs signature to forecast the TNBC prognosis.The association of the GARGs signature with immune infiltrates and drug sensitivity was further explored.Results:In total,430 DEGARGs were identified between TNBC and healthy samples,among which 20 were related to TNBC prognosis.Two GARG-related molecular clusters associated with different survival times and immune heterogeneity were identified.A risk model for TNBC was established based on six GARGs,and the high-risk(HR)group exhibited a poor prognosis.The HR group demonstrated a distinctly high M2 macrophage infiltration and low M1 macrophage infiltration,which contributed to an immunosuppressive tumor microenvironment and thus led to poor prognosis of the HR group.Immune dysfunction scores and programmed cell death ligand 1(PD-L1)expression were substantially elevated in the HR group.The HR group showed increased sensitivity to anticancer drugs,such as cisplatin.Conclusion:Our findings suggest that GARGs are involved in the pathogenesis of TNBC and provide new insights into prognostic prediction.The identified clusters and GARGs signatures have the potential to guide individualized therapy.展开更多
BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initi...BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initiation,progression,metastasis,therapeutic resistance,and disease recurrence.Cancer stem cells possess selfrenewal capacity,multipotent differentiation potential,and enhanced tumorigenic activity,but their molecular characteristics and regulatory mechanisms require further investigation.AIM To comprehensively characterize the molecular features of BCSCs through multiomics approaches,construct a prognostic prediction model based on stem cellrelated genes,reveal cell-cell communication networks within the tumor microenvironment,and provide theoretical foundation for personalized treatment strategies.METHODS Flow cytometry was employed to detect the expression of BCSC surface markers(CD34,CD45,CD29,CD90,CD105).Transcriptomic analysis was performed to identify differentially expressed genes.Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model.Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns.Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.RESULTS Flow cytometric analysis revealed the highest expression of CD105(96.30%),followed by CD90(68.43%)and CD34(62.64%),while CD29 showed lower expression(7.16%)and CD45 exhibited the lowest expression(1.19%).Transcriptomic analysis identified 3837 significantly differentially expressed genes(1478 upregulated and 2359 downregulated).Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes,and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups(P<0.001).Single-cell analysis revealed tumor cellular heterogeneity,and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes.MED18 gene showed significantly higher expression in malignant tissues(P<0.001)and occupied a central position in cell-cell communication networks,exhibiting significant correlations with tumor cells,macrophages,fibroblasts,and endothelial cells.CONCLUSION This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches,identified reliable surface markers and key regulatory genes,and constructed a prognostic prediction model with clinical application value.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 It remains controversial as to which pathological classification is most valuable in predicting the overall survival(OS)of patients with gastric cancer(GC).AIM To assess the prognostic performances of three...BACKGROUND It remains controversial as to which pathological classification is most valuable in predicting the overall survival(OS)of patients with gastric cancer(GC).AIM To assess the prognostic performances of three pathological classifications in GC and develop a novel prognostic nomogram for individually predicting OS.METHODS Patients were identified from the Surveillance,Epidemiology,and End Results program.Univariate and multivariate analyses were performed to identify the independent prognostic factors.Model discrimination and model fitting were evaluated by receiver operating characteristic curves and Akaike information criteria.Decision curve analysis was performed to assess clinical usefulness.The independent prognostic factors identified by multivariate analysis were further applied to develop a novel prognostic nomogram.RESULTS A total of 2718 eligible GC patients were identified.The modified Lauren classification was identified as one of the independent prognostic factors for OS.It showed superior model discriminative ability and model-fitting performance over the other pathological classifications,and similar results were obtained in various patient settings.In addition,it showed superior net benefits over the Lauren classification and tumor differentiation grade in predicting 3-and 5-year OS.A novel prognostic nomogram incorporating the modified Lauren classification showed superior model discriminative ability,model-fitting performance,and net benefits over the American Joint Committee on Cancer 8th edition tumor-nodemetastasis classification.CONCLUSION The modified Lauren classification shows superior net benefits over the Lauren classification and tumor differentiation grade in predicting OS.A novel prognostic nomogram incorporating the modified Lauren classification shows good model discriminative ability,model-fitting performance,and net benefits.展开更多
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 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This work was supported by the Special Support Program of Sun Yat-sen University Cancer Center(PT19020401)the Science and Technology Planning Project of Guangzhou,China(202002030205)the Clinical Oncology Foundation of Chinese Society of Clinical Oncology(Y-XD2019-124).
文摘Adult T-cell lymphoblastic lymphoma(T-LBL)is a rare and aggressive subtype of non-Hodgkin’s lymphoma that differs from pediatric T-LBL and has a worse prognosis.Due to its rarity,little is known about the genetic and molecular characteristics,optimal treatment modalities,and prognostic factors of adult T-LBL.Therefore,we summarized the existing studies to comprehensively discuss the above issues in this review.Genetic mutations of NOTCH1/FBXW7,PTEN,RAS,and KMT2D,together with abnormal activation of signaling pathways,such as the JAK-STAT signaling pathway were described.We also discussed the therapeutic modalities.Once diagnosed,adult T-LBL patients should receive intensive or pediatric acute lymphoblastic leukemia regimen and central nervous system prophylaxis as soon as possible,and cranial radiation-free protocols are appropriate.Mediastinal radiotherapy improves clinical outcomes,but adverse events are of concern.Hematopoietic stem cell transplantation may be considered for adult T-LBL patients with high-risk factors or those with relapsed/refractory disease.Besides,several novel prognostic models have been constructed,such as the 5-miRNAs-based classifier,11-gene-based classifier,and 4-CpG-based classifier,which have presented significant prognostic value in adult T-LBL.
文摘This editorial narrative review discussed Budd-Chiari syndrome(BCS),which re-presents a rare but critical vascular liver disease resulting in an obstruction of he-patic venous outflow.Despite having a unifying mechanism,the syndrome shows a large heterogeneity across presentation,cause,and disease trajectory,compli-cating diagnosis and management.Based on established prognostic scoring systems,the New Clichy Score,the BCS-transjugular intrahepatic portosystemic shunt Index,the Zeitoun Score,and the Pediatric End-stage Liver Disease score were examined.These scoring systems are used for risk stratification and thera-peutic decision-making.Although these models deliver suitability information,their static parameters,narrow validation,and limited generalizability reduce their usefulness in diverse populations.Specific challenges are highlighted in pediatric patients,pregnant females,and individuals with myeloproliferative neoplasms for whom current tools often fall short.Moreover,there remains uncertainty regarding the durability of Pediatric End-stage Liver Disease score response and longer-term risks,such as hepatocellular carcinoma.There is a need to have a dynamic prognostic model that uses imaging and genetic factors in future studies.The article discussed enhancing recruitment to improve research.Overall,this article provided a contemporary,evidence-based approach for cli-nicians to aid in the evaluation and treatment of BCS.
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 National Natural Science Foundation of China,No.81802777.
文摘BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.
基金the Shanghai Municipal Key Clinical Specialty Program(No.shslczdzk06302)。
文摘Ovarian cancer has one of the highest mortality rates among gynecological malignancies.This disease has a low early detection rate,a high postoperative recurrence rate,and a 5-year survival rate of only 40%.Hence,there is an urgent need to improve the early diagnosis and prognosis of ovarian cancer.Prediction models can effectively estimate the risk of disease occurrence,as well as its prognosis.Recently,many studies have established multiple ovarian cancer prediction models based on different regions and populations.These models can improve the detection rate and optimize the prognosis management to a certain extent.Herein,the construction principle of the ovarian cancer risk prediction model and its validation are summarized;furthermore,comprehensive reviews and comparisons of the different types of these models are made.Therefore,our review may be of great significance for the whole course of ovarian cancer management.
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 the National Natural Science Foundation of China,No.82473285Beijing Hope Run Special Fund of Cancer Foundation of China,No.LC2022B02.
文摘BACKGROUND Gastric signet-ring cell carcinoma(GSRCC)is a more aggressive subtype of gastric cancer compared to gastric adenocarcinoma(GA),with an increasing incidence.However,the prognostic differences between these subtypes,particularly in re-sectable cases,remain unclear.AIM To evaluate prognostic factors and develop a predictive model for GA and GSRCC patients undergoing curative resection.METHODS This retrospective cohort study included patients with GA and GSRCC who underwent curative surgery at the National Cancer Center/Cancer Hospital,Chinese Academy of Medical Sciences,from 2011 to 2018.Propensity score ma-tching(PSM)(1:1)balanced the baseline characteristics.Prognostic factors were identified using univariate and multivariate Cox and least absolute shrinkage and selection operator(LASSO)regression analyses.Model performance was eva-luated through calibration curves,decision curve analysis(DCA),and time-dependent receiver operating characteristic curves.Subgroup analysis and Ka-plan-Meier survival curves were generated.RESULTS In a cohort of 3027 patients,the GSRCC group was characterized by a significantly higher prevalence of individuals under 60 years of age,females,cases with poor differentiation,and early-stage(stage I)disease(all P<0.001).After PSM,the baseline was balanced and 761 patients were retained in each group.Variables identified through univariate Cox regression were included in the LASSO regression analysis.Mul-tivariate Cox regression analysis identified age,tumor differentiation,tumor size,vascular invasion,and post-treatment nodal margin staging as independent prognostic factors.Subgroup analysis indicated a notably poorer prognosis for GSRCC in patients aged 60 and above(hazard ratio=1.36,P=0.025).The nomogram(C-index=0.755)exhibited greater predictive accuracy than tumor node metastasis(TNM)staging for 1-,3-,and 5-year overall survival(all P<0.001),and provided a higher clinical net benefit according to DCA.CONCLUSION This study systematically compared resectable GA and GSRCC,revealing no overall survival difference.However,GSRCC demonstrated a significantly elevated mortality risk in subgroups stratified by age and tumor size.Multivariate analysis identified age,differentiation,tumor size,vascular invasion,and TNM stage as independent prognostic factors.The nomogram integrates clinicopathological features for precise risk stratification,surpassing traditional TNM staging.
基金Key Research and Development Foundation supported by Science and Technology Department of Sichuan Province.Project Number:2023YFS0243Project Name:Application of Multiple Nucleic Acid Detection of Respiratory Pathogens Based on Multiple Fusion Curve Technology in Rapid Pathogenic Analysis of Acute Respiratory Distress Syndrome Patients Caused By Atypical Pathogen Infections in Emergency Departments+1 种基金Fund Name:Applied Basic Research Foundation supported by Science and Technology Department of Sichuan Province.Project Number:2021YJ0135Project Name:Explorating the Antiinflammatory Mechanism of Extracellular Vesicles Secreted by Wharton’s Jelly Mesenchymal Stem Cells in Alleviating Pulmonary Vascular Endothelial Injury and Discussing the Effectiveness of VEGF Gene Modification in Sepsis-related ALI/ARDS.
文摘Background:The centrosome,a crucial cellular structure involved in the mitotic process of eukaryotic cells,plays a significant role in tumor progression by regulating the growth and differentiation of neoplastic cells.This makes the centrosome a promising target for therapeutic strategies in cancer treatment.Methods:Utilizing data from the TCGA database,we identified centrosome-related genes and constructed a prognostic model for 518 lung adenocarcinoma patients.Prognosis-associated genes were initially screened using univariate Cox regression,with overfitting minimized by applying LASSO regression to remove collinearity.Finally,a set of 12 genes was selected through multivariable Cox regression for inclusion in the prognostic model.Results:The model’s performance was assessed using ROC curve analysis,demonstrating a robust predictive ability with an AUC of 0.728 in the training group and 0.695 in the validation group.Differential expression analysis between high-risk(HRLAs)and low-risk(LRLAs)individuals was performed,followed by enrichment analyses using KEGG,GO,Progeny,GSVA,and GSEA.These analyses revealed significant differences in immune-related pathways between the two groups.Immune microenvironment assessment through ssGSEA and ESTIMATE indicated that individuals with poor prognosis exhibited lower immune,stromal,and ESTIMATE scores,along with higher tumor purity,suggesting an impaired immune microenvironment in HRLAs patients.Drug susceptibility analysis and molecular docking showed that HRLAs individuals were more responsive to docetaxel,emphasizing the therapeutic relevance of paclitaxel in this cohort.Conclusion:We successfully developed and validated a centrosome-associated gene-based prognostic model,offering clinicians valuable insights for improved decision-making and personalized treatment strategies.This model may facilitate the identification of high-risk patients and guide therapeutic interventions in lung adenocarcinoma.
基金Supported by National Natural Science Foundation of China,No.82300694 and No.81970523Natural Science Foundation of Hunan Province,No.2022JJ70165,No.2021JJ31067,No.2023JJ40828 and No.2022JJ40704.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is a significant global health challenge with rising incidence rates and poor prognoses.Aminoacyl-tRNA synthetases(ARSs)are important regulators implicated in the occurrence and progression of several cancers.However,their specific function in HCC remains unclear,and ARSs-related prognostic factors for patient stratification are lacking.AIM To investigate the ARSs-related mechanisms of HCC and establish an effective prognostic risk model for stratifying patients with HCC.METHODS We screened ARSs genes of interest using differential gene expression,mutation,and survival analysis.Western blot and Immunohistochemistry were used to analyze MARS1 expression in the liver tissues of patients with HCC.Functional studies,including CCK-8 cell viability assay,EdU cell proliferation assay,cell cycle assays,Transwell migration and invasion assays,and in vivo tumor xenograft models,were conducted to comprehensively elucidate the specific role of MARS1 in HCC.Moreover,the MARS1-related prognostic score(MRPS)was established by LASSO regression and Cox regression analysis in The Cancer Genome Atlas-HCC and GSE14520 cohorts.Patients’immunotherapy and chemotherapy responses were predicted by immunomicroenvironment and drug susceptibility analysis in both subgroups.RESULTS MARS1 was selected as a target gene from a series of ARSs genes,with markedly higher expression observed in HCC tissues compared to adjacent non-cancerous tissues.Silencing MARS1 considerably impeded the proliferation,migration,invasion,and tumorigenic abilities of HCC cells in vitro and in vivo.Moreover,high MRPSs were associated with poor overall survival,altered infiltration of T cells,macrophages,monocytes,elevated immune checkpoint expression(PD-L1,CTLA4,LAG3),and reduced drug sensitivity in HCC.CONCLUSION MARS1 promotes HCC development and represents a potential therapeutic target for HCC management.Furthermore,MRPS serves as an independent prognostic factor for survival and a predictor of tumor treatment response.
文摘Background:Primary liver cancer poses a significant global health burden,with projections indicating a surpassing of one million cases by 2025.Cuproptosis,a copper-dependent mechanism of cell death,plays a crucial role in the pathogenesis,progression,and prognosis of various cancers,including hepatocellular carcinoma(HCC).Purpose:This study aimed to develop a prognostic model for HCC based on cuproptosis-related genes,utilizing clinical data and gene expression profiles from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases.Materials and Methods:Clinical features and gene expression data of HCC patients were collected from publicly available databases.Patients from TCGA were randomly divided into training and testing sets,and Lasso Cox regression was applied to develop a predictive model using cuproptosis-related genes.Results:The analysis identified Copper Metabolism Domain Containing 1(COMMD1)as a potential prognostic marker for HCC,with deletion of this gene impacting disease progression.Cellular functional experiments validated the role of COMMD1 in HCC.Conclusions:COMMD1 emerges as a promising candidate for HCC treatment,with implications for prognosis prediction and therapeutic targeting.
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.
文摘BACKGROUND Gastric cancer,a globally prevalent malignant tumor,continues to exhibit high incidence and mortality rates.Although radical gastrectomy remains the primary treatment for this disease,postoperative complications frequently arise,negatively impacting short-term recovery and significantly reducing patients’quality of life.In this context,accurately predicting the risk of postoperative recurrence and metastasis,coupled with targeted interventions,could substantially improve patient outcomes.The C-reactive protein-triglyceride-glucose index(CTI),a composite biomarker that integrates metabolic disturbances and systemic inflammation,has garnered increasing attention in oncology.The prognostic nutritional index(PNI),a composite measure based on serum albumin and peripheral blood lymphocyte count,is used to evaluate both the nutritional status and systemic immune function of patients.In recent years,both the CTI and PNI have demonstrated significant prognostic value in predicting tumor outcomes,assessing treatment responses,and formulating personalized treatment strategies.AIM To investigate whether the combined inflammation and insulin resistance marker,the CTI,can serve as a prognostic indicator for patients undergoing radical gastrectomy for gastric cancer.Additionally,it seeks to develop a predictive model by incorporating the PNI alongside CTI.METHODS This retrospective study included a total of 300 patients who underwent radical gastrectomy.The patients were classified into high and low CTI groups based on their CTI index.Cox proportional hazards regression analysis was performed to identify independent prognostic factors influencing overall survival(OS)and disease-free survival(DFS),and two nomogram models were developed.RESULTS Of the included patients,131 had a high CTI and 169 had a low CTI.The DFS period of the low CTI group was significantly longer than that of the high CTI group.The number of postoperative adjuvant treatments,T stage,N stage,CTI,and PNI were identified as independent prognostic factors for DFS.The hazard ratio for CTI was 2.07(95%confidence interval:1.36-3.17,P<0.001).In terms of OS,the OS period of the low CTI group was significantly longer than that of the high CTI group.Whether adjuvant treatment was administered,T stage,CTI,and PNI were independent prognostic factors for OS.The hazard ratio for CTI was 2.47(95%confidence interval:1.44-4.23,P=0.001).The nomogram models for OS and DFS further emphasized the importance of CTI as a key predictor of patient prognosis.CONCLUSION CTI is a long-term prognostic indicator for the outcome of radical gastrectomy for gastric cancer.Patients with lower CTI values have a better prognosis.The prediction models constructed by combining CTI with PNI has good predictive ability for DFS and OS after radical gastrectomy.
文摘Objectives:Triple-negative breast cancer(TNBC)presents a major treatment challenge due to its aggressive behavior.The dysfunction of the Golgi apparatus(GA)contributes to the development of various cancers.This study aimed to utilize GA-related genes(GARGs)to forecast the prognosis and immune profile of TNBC.Methods:The data were downloaded from The Cancer Genome Atlas(TCGA)database,including 175 TNBC and 99 healthy samples.The differentially expressed GARGs(DEGARGs)were analyzed using the TCGA biolinks package.The patients with TNBC were classified into two clusters utilizing the ConsensusClusterPlus package according to prognosis-related DEGARGs,followed by comparing the differences in prognosis and immune infiltration between the two clusters.Next,LASSO and stepwise Cox regression were applied to establish a GARGs signature to forecast the TNBC prognosis.The association of the GARGs signature with immune infiltrates and drug sensitivity was further explored.Results:In total,430 DEGARGs were identified between TNBC and healthy samples,among which 20 were related to TNBC prognosis.Two GARG-related molecular clusters associated with different survival times and immune heterogeneity were identified.A risk model for TNBC was established based on six GARGs,and the high-risk(HR)group exhibited a poor prognosis.The HR group demonstrated a distinctly high M2 macrophage infiltration and low M1 macrophage infiltration,which contributed to an immunosuppressive tumor microenvironment and thus led to poor prognosis of the HR group.Immune dysfunction scores and programmed cell death ligand 1(PD-L1)expression were substantially elevated in the HR group.The HR group showed increased sensitivity to anticancer drugs,such as cisplatin.Conclusion:Our findings suggest that GARGs are involved in the pathogenesis of TNBC and provide new insights into prognostic prediction.The identified clusters and GARGs signatures have the potential to guide individualized therapy.
基金the Natural Science Foundation of Yongchuan District,No.2023yc-jckx20021.
文摘BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initiation,progression,metastasis,therapeutic resistance,and disease recurrence.Cancer stem cells possess selfrenewal capacity,multipotent differentiation potential,and enhanced tumorigenic activity,but their molecular characteristics and regulatory mechanisms require further investigation.AIM To comprehensively characterize the molecular features of BCSCs through multiomics approaches,construct a prognostic prediction model based on stem cellrelated genes,reveal cell-cell communication networks within the tumor microenvironment,and provide theoretical foundation for personalized treatment strategies.METHODS Flow cytometry was employed to detect the expression of BCSC surface markers(CD34,CD45,CD29,CD90,CD105).Transcriptomic analysis was performed to identify differentially expressed genes.Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model.Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns.Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.RESULTS Flow cytometric analysis revealed the highest expression of CD105(96.30%),followed by CD90(68.43%)and CD34(62.64%),while CD29 showed lower expression(7.16%)and CD45 exhibited the lowest expression(1.19%).Transcriptomic analysis identified 3837 significantly differentially expressed genes(1478 upregulated and 2359 downregulated).Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes,and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups(P<0.001).Single-cell analysis revealed tumor cellular heterogeneity,and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes.MED18 gene showed significantly higher expression in malignant tissues(P<0.001)and occupied a central position in cell-cell communication networks,exhibiting significant correlations with tumor cells,macrophages,fibroblasts,and endothelial cells.CONCLUSION This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches,identified reliable surface markers and key regulatory genes,and constructed a prognostic prediction model with clinical application value.
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 The China Scholarship Council,No.201908050148.
文摘BACKGROUND It remains controversial as to which pathological classification is most valuable in predicting the overall survival(OS)of patients with gastric cancer(GC).AIM To assess the prognostic performances of three pathological classifications in GC and develop a novel prognostic nomogram for individually predicting OS.METHODS Patients were identified from the Surveillance,Epidemiology,and End Results program.Univariate and multivariate analyses were performed to identify the independent prognostic factors.Model discrimination and model fitting were evaluated by receiver operating characteristic curves and Akaike information criteria.Decision curve analysis was performed to assess clinical usefulness.The independent prognostic factors identified by multivariate analysis were further applied to develop a novel prognostic nomogram.RESULTS A total of 2718 eligible GC patients were identified.The modified Lauren classification was identified as one of the independent prognostic factors for OS.It showed superior model discriminative ability and model-fitting performance over the other pathological classifications,and similar results were obtained in various patient settings.In addition,it showed superior net benefits over the Lauren classification and tumor differentiation grade in predicting 3-and 5-year OS.A novel prognostic nomogram incorporating the modified Lauren classification showed superior model discriminative ability,model-fitting performance,and net benefits over the American Joint Committee on Cancer 8th edition tumor-nodemetastasis classification.CONCLUSION The modified Lauren classification shows superior net benefits over the Lauren classification and tumor differentiation grade in predicting OS.A novel prognostic nomogram incorporating the modified Lauren classification shows good model discriminative ability,model-fitting performance,and net benefits.
