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
A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily av...A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily available clinical variables—albumin level,aspartate aminotransferase level,white blood cell count,ascites,portal vein thrombosis,and bleeding signs—and demonstrated promising predictive performance.However,limitations,including the retrospective design and exclusion of patients with hepatocellular carcinoma,may affect the generaliz-ability of the model.Additionally,further improvement is needed in the model’s discrimination between intermediate-and high-risk groups in external.Prospec-tive validation and inclusion of additional variables are recommended to enhan-ce predictive accuracy across diverse clinical scenarios.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
Objectives:Despite the fact that prostate cancer is one of the most common tumors in men,this study intends to evaluate the predictive significance of immune andmetabolic genes in prostate cancer usingmulti-omics data...Objectives:Despite the fact that prostate cancer is one of the most common tumors in men,this study intends to evaluate the predictive significance of immune andmetabolic genes in prostate cancer usingmulti-omics data and experimental validation.Methods:The research developed and validated a prognostic model utilizing The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases,integrating immune andmetabolic gene sets.Additionally,the prognostic gene Adenylate Kinase 5(AK5)was analyzed in prostate cancer tissue microarrays from RuijinHospital.The functional role of the AK5 gene was validated through knockdown and overexpression experiments in four prostate cancer cell lines,employing cell proliferation assays,colony formation assays,and both xenograftmodels in nude mice and patient-derived xenograft models.Results:This research developed a prognostic model comprising ten genes,which was validated across multiple datasets for its predictive efficacy.Experimental results indicated that AK5 is significantly expressed in prostate cancer and facilitates tumor proliferation;knockdown of AK5 inhibited cell colony formation and growth of subcutaneous xenografts in nude mice,while AK5 inhibitors significantly reduced tumor volume in patient-derived xenografts.Conclusion:This study constructed a prognostic model with clinical potential and preliminarily confirmed the oncogenic role of AK5 in prostate cancer.The findings indicate that focusing on the immunological metabolic axis and the AK5 gene may offer novel approaches for prostate cancer treatment.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 Budd-Chiari syndrome(BCS)is caused by obstruction of the hepatic veins or suprahepatic inferior vena cava,leading to portal hypertension and the development of gastroesophageal varices(GEVs),which are assoc...BACKGROUND Budd-Chiari syndrome(BCS)is caused by obstruction of the hepatic veins or suprahepatic inferior vena cava,leading to portal hypertension and the development of gastroesophageal varices(GEVs),which are associated with an increased risk of bleeding.Existing risk models for variceal bleeding in cirrhotic patients have limited applicability to BCS due to differences in pathophysiology.Radiomics,as a noninvasive technique,holds promise as a tool for more accurate prediction of bleeding risk in BCS-related GEVs.AIM To develop and validate a personalized risk model for predicting variceal bleeding in BCS patients with GEVs.METHODS We retrospectively analyzed clinical data from 444 BCS patients with GEVs in two centers.Radiomic features were extracted from portal venous phase computed tomography(CT)scans.A training cohort of 334 patients was used to develop the model,with 110 patients serving as an external validation cohort.LASSO Cox regression was used to select radiomic features for constructing a radiomics score(Radscore).Univariate and multivariate Cox regression identified independent clinical predictors.A combined radiomics+clinical(R+C)model was developed using stepwise regression.Model performance was assessed using the area under the receiver operating characteristic curve(AUC),calibration plots,and decision curve analysis(DCA),with external validation to evaluate generalizability.RESULTS The Radscore comprised four hepatic and six splenic CT features,which predicted the risk of variceal bleeding.Multivariate analysis identified invasive treatment to relieve hepatic venous outflow obstruction,anticoagulant therapy,and hemoglobin levels as independent clinical predictors.The R+C model achieved C-indices of 0.906(training)and 0.859(validation),outperforming the radiomics and clinical models alone(AUC:training 0.936 vs 0.845 vs 0.823;validation 0.876 vs 0.712 vs 0.713).DCA showed higher clinical net benefit across the thresholds.The model stratified patients into low-,medium-and high-risk groups with significant differences in bleeding rates(P<0.001).An online tool is available at https://bcsvh.shinyapps.io/BCS_Variceal_Bleeding_Risk_Tool/.CONCLUSION We developed and validated a novel radiomics-based model that noninvasively and conveniently predicted risk of variceal bleeding in BCS patients with GEVs,aiding early identification and management of high-risk patients.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
This editorial comment is on the article by Xu et al.It offers an in-depth analysis of liver function assessment tools and their prognostic roles in non-malignant liver diseases,with a focus on the albumin-bilirubin(A...This editorial comment is on the article by Xu et al.It offers an in-depth analysis of liver function assessment tools and their prognostic roles in non-malignant liver diseases,with a focus on the albumin-bilirubin(ALBI)score.ALBI’s components,grading system,and clinical relevance across various liver conditions are reviewed and compared with traditional models such as the Child-Pugh and model for end-stage liver disease scores.We included recent studies evaluating ALBI’s role in estimating liver function,suggesting it may help differentiate patients who appear similar under other staging systems,and assist in guiding clinical decisions.Although ALBI is primarily used as an indicator of hepatic reservoir in hepatocellular carcinoma,it has been demonstrated a positive correlation with overall survival,tumor recurrence,and post-hepatectomy liver failure in patients undergoing potentially curative treatments such as liver resection,liver transplantation,and local ablation.Moreover,several studies suggest that ALBI can also predict survival outcomes,treatment-related toxicity,and liver-related complications in patients receiving trans-arterial chemoembolization,radioembolization,external-beam radiotherapy,or systemic therapies.Its growing use in nonmalignant liver diseases,including primary biliary cholangitis,cirrhosis,acute and chronic liver failure,and viral hepatitis highlights the need for large,prospective studies.Further studies are warranted to validate the integration of ALBI into routine clinical practice and to clarify its role in guiding prognosis and treatment planning.展开更多
Objectives:Colorectal cancer(CRC)remains a major contributor to global cancer mortality,ranking second worldwide for cancer-related deaths in 2022,and is characterized by marked heterogeneity in prognosis and therapeu...Objectives:Colorectal cancer(CRC)remains a major contributor to global cancer mortality,ranking second worldwide for cancer-related deaths in 2022,and is characterized by marked heterogeneity in prognosis and therapeutic response.We sought to construct a machine-learning prognosticmodel based on a complement-related risk signature(CRRS)and to situate this signature within the CRC immune microenvironment.Methods:Transcriptomic profiles with matched clinical annotations from TCGA and GEO CRC cohorts were analyzed.Prognostic CRRS genes were screened using Cox proportional hazards modeling alongside machine-learning procedures.A random survival forest(RSF)predictor was trained and externally validated.Comparisons of immune infiltration,mutational burden,pathway enrichment,and drug sensitivity were made between risk groups.The function of FAM84A,a key model gene,was examined in CRC cell lines.Results:The six-gene CRRS model accurately stratified patients by survival outcomes.Low-risk patients exhibited greater immune cell infiltration and higher predicted response to immunotherapy and chemotherapy,while high-risk patients showed enrichment of complement activation and matrix remodeling pathways.FAM84A was shown to promote CRC cell proliferation,migration,and epithelial–mesenchymal transition.Conclusion:CRRS is a critical modulator of the CRC immune microenvironment.The developed model enables precise risk prediction and supports individualized therapeutic decisions in CRC.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 Hepatocellular carcinoma(HCC)is a common cancer with a poor prognosis.Previous studies revealed that the tumor microenvironment(TME)plays an important role in HCC progression,recurrence,and metastasis,leadi...BACKGROUND Hepatocellular carcinoma(HCC)is a common cancer with a poor prognosis.Previous studies revealed that the tumor microenvironment(TME)plays an important role in HCC progression,recurrence,and metastasis,leading to poor prognosis.However,the effects of genes involved in TME on the prognosis of HCC patients remain unclear.Here,we investigated the HCC microenvironment to identify prognostic genes for HCC.AIM To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC.METHODS We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm.Additionally,a risk score model was established based on Differentially Expressed Genes(DEGs)between high and lowimmune/stromal score patients.RESULTS The risk score model consisting of eight genes was constructed and validated in the HCC patients.The patients were divided into high-or low-risk groups.The genes(Disabled homolog 2,Musculin,C-X-C motif chemokine ligand 8,Galectin 3,B-cell-activating transcription factor,Killer cell lectin like receptor B1,Endoglin and adenomatosis polyposis coli tumor suppressor)involved in our risk score model were considered to be potential immunotherapy targets,and they may provide better performance in combination.Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway,respectively,related to the immune-related genes in the DEGs between high-and low-risk groups.The receiver operating characteristic(ROC)curve analysis confirmed the good potency of the risk score prognostic model.Moreover,we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database.A nomogram was established to predict the overall survival of HCC patients.CONCLUSION The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.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:Due to the high heterogeneity among hepatocellular carcinoma(HCC)patients receiving transarterial chemoembolization(TACE),the prognosis of patients varies significantly.The decisionmaking on the initiation ...Background:Due to the high heterogeneity among hepatocellular carcinoma(HCC)patients receiving transarterial chemoembolization(TACE),the prognosis of patients varies significantly.The decisionmaking on the initiation and/or repetition of TACE under different liver functions is a matter of concern in clinical practice.Thus,we aimed to develop a prediction model for TACE candidates using risk stratification based on varied liver function.Methods:A total of 222 unresectable HCC patients who underwent TACE as their only treatment were included in this study.Cox proportional hazards regression was performed to select the independent risk factors and establish a predictive model for the overall survival(OS).The model was validated in patients with different Child-Pugh class and compared to previous TACE scoring systems.Results:The five independent risk factors,including alpha-fetoprotein(AFP)level,maximal tumor size,the increase of albumin-bilirubin(ALBI)grade score,tumor response,and the increase of aspartate aminotransferase(AST),were used to build a prognostic model(ASARA).In the training and validation cohorts,the OS of patients with ASARA score≤2 was significantly higher than that of patients with ASARA score>2(P<0.001,P=0.006,respectively).The ASARA model and its modified version“AS(ARA)”can effectively distinguish the OS(P<0.001,P=0.004)between patients with Child-Pugh class A and B,and the C-index was 0.687 and 0.706,respectively.For repeated TACE,the ASARA model was superior to Assessment for Retreatment with TACE(ART)and ALBI grade,maximal tumor size,AFP,and tumor response(ASAR)among Child-Pugh class A patients.For the first TACE,the performance of AS(ARA)was better than that of modified hepatoma arterial-embolization prognostic(mHAP),mHAP3,and ASA(R)models among Child-Pugh class B patients.Conclusions:The ASARA scoring system is valuable in the decision-making of TACE repetition for HCC patients,especially Child-Pugh class A patients.The modified AS(ARA)can be used to screen the ideal candidate for TACE initiation in Child-Pugh class B patients with poor liver function.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
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
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 Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 6...BACKGROUND Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 60%of GC are linked to infection with Helicobacter pylori(H.pylori),a gram-negative,active,microaerophilic,and helical bacterium.This parasite induces GC by producing toxic factors,such as cytotoxin-related gene A,vacuolar cytotoxin A,and outer membrane proteins.Ferroptosis,or iron-dependent programmed cell death,has been linked to GC,although there has been little research on the link between H.pylori infection-related GC and ferroptosis.AIM To identify coregulated differentially expressed genes among ferroptosis-related genes(FRGs)in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.METHODS Gene expression profiles of GC patients and those with H.pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus(GEO)databases.The FRGs were acquired from the FerrDb database.A ferroptosis-related gene prognostic index(FRGPI)was created using least absolute shrinkage and selection operator–Cox regression.The predictive ability of the FRGPI was validated in the GEO cohort.Finally,we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.RESULTS Four hub genes were identified(NOX4,MTCH1,GABARAPL2,and SLC2A3)and shown to accurately predict GC and H.pylori-associated GC.The FRGPI based on the hub genes could independently predict GC patient survival;GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group.The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression.Moreover,the gene expression levels of common immune checkpoint proteins dramatically increased in the highrisk subgroup of the FRGPI cohort.The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane.The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.CONCLUSION In this study,we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.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.
文摘A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily available clinical variables—albumin level,aspartate aminotransferase level,white blood cell count,ascites,portal vein thrombosis,and bleeding signs—and demonstrated promising predictive performance.However,limitations,including the retrospective design and exclusion of patients with hepatocellular carcinoma,may affect the generaliz-ability of the model.Additionally,further improvement is needed in the model’s discrimination between intermediate-and high-risk groups in external.Prospec-tive validation and inclusion of additional variables are recommended to enhan-ce predictive accuracy across diverse clinical scenarios.
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 the National Natural Science Foundation of China(grant number:No.82173045).
文摘Objectives:Despite the fact that prostate cancer is one of the most common tumors in men,this study intends to evaluate the predictive significance of immune andmetabolic genes in prostate cancer usingmulti-omics data and experimental validation.Methods:The research developed and validated a prognostic model utilizing The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases,integrating immune andmetabolic gene sets.Additionally,the prognostic gene Adenylate Kinase 5(AK5)was analyzed in prostate cancer tissue microarrays from RuijinHospital.The functional role of the AK5 gene was validated through knockdown and overexpression experiments in four prostate cancer cell lines,employing cell proliferation assays,colony formation assays,and both xenograftmodels in nude mice and patient-derived xenograft models.Results:This research developed a prognostic model comprising ten genes,which was validated across multiple datasets for its predictive efficacy.Experimental results indicated that AK5 is significantly expressed in prostate cancer and facilitates tumor proliferation;knockdown of AK5 inhibited cell colony formation and growth of subcutaneous xenografts in nude mice,while AK5 inhibitors significantly reduced tumor volume in patient-derived xenografts.Conclusion:This study constructed a prognostic model with clinical potential and preliminarily confirmed the oncogenic role of AK5 in prostate cancer.The findings indicate that focusing on the immunological metabolic axis and the AK5 gene may offer novel approaches for prostate cancer treatment.
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 Natural Science Foundation of Henan Province,China,No.232300420232Henan Provincial Key Research and Development Project,No.231111313500.
文摘BACKGROUND Budd-Chiari syndrome(BCS)is caused by obstruction of the hepatic veins or suprahepatic inferior vena cava,leading to portal hypertension and the development of gastroesophageal varices(GEVs),which are associated with an increased risk of bleeding.Existing risk models for variceal bleeding in cirrhotic patients have limited applicability to BCS due to differences in pathophysiology.Radiomics,as a noninvasive technique,holds promise as a tool for more accurate prediction of bleeding risk in BCS-related GEVs.AIM To develop and validate a personalized risk model for predicting variceal bleeding in BCS patients with GEVs.METHODS We retrospectively analyzed clinical data from 444 BCS patients with GEVs in two centers.Radiomic features were extracted from portal venous phase computed tomography(CT)scans.A training cohort of 334 patients was used to develop the model,with 110 patients serving as an external validation cohort.LASSO Cox regression was used to select radiomic features for constructing a radiomics score(Radscore).Univariate and multivariate Cox regression identified independent clinical predictors.A combined radiomics+clinical(R+C)model was developed using stepwise regression.Model performance was assessed using the area under the receiver operating characteristic curve(AUC),calibration plots,and decision curve analysis(DCA),with external validation to evaluate generalizability.RESULTS The Radscore comprised four hepatic and six splenic CT features,which predicted the risk of variceal bleeding.Multivariate analysis identified invasive treatment to relieve hepatic venous outflow obstruction,anticoagulant therapy,and hemoglobin levels as independent clinical predictors.The R+C model achieved C-indices of 0.906(training)and 0.859(validation),outperforming the radiomics and clinical models alone(AUC:training 0.936 vs 0.845 vs 0.823;validation 0.876 vs 0.712 vs 0.713).DCA showed higher clinical net benefit across the thresholds.The model stratified patients into low-,medium-and high-risk groups with significant differences in bleeding rates(P<0.001).An online tool is available at https://bcsvh.shinyapps.io/BCS_Variceal_Bleeding_Risk_Tool/.CONCLUSION We developed and validated a novel radiomics-based model that noninvasively and conveniently predicted risk of variceal bleeding in BCS patients with GEVs,aiding early identification and management of high-risk patients.
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.
文摘This editorial comment is on the article by Xu et al.It offers an in-depth analysis of liver function assessment tools and their prognostic roles in non-malignant liver diseases,with a focus on the albumin-bilirubin(ALBI)score.ALBI’s components,grading system,and clinical relevance across various liver conditions are reviewed and compared with traditional models such as the Child-Pugh and model for end-stage liver disease scores.We included recent studies evaluating ALBI’s role in estimating liver function,suggesting it may help differentiate patients who appear similar under other staging systems,and assist in guiding clinical decisions.Although ALBI is primarily used as an indicator of hepatic reservoir in hepatocellular carcinoma,it has been demonstrated a positive correlation with overall survival,tumor recurrence,and post-hepatectomy liver failure in patients undergoing potentially curative treatments such as liver resection,liver transplantation,and local ablation.Moreover,several studies suggest that ALBI can also predict survival outcomes,treatment-related toxicity,and liver-related complications in patients receiving trans-arterial chemoembolization,radioembolization,external-beam radiotherapy,or systemic therapies.Its growing use in nonmalignant liver diseases,including primary biliary cholangitis,cirrhosis,acute and chronic liver failure,and viral hepatitis highlights the need for large,prospective studies.Further studies are warranted to validate the integration of ALBI into routine clinical practice and to clarify its role in guiding prognosis and treatment planning.
文摘Objectives:Colorectal cancer(CRC)remains a major contributor to global cancer mortality,ranking second worldwide for cancer-related deaths in 2022,and is characterized by marked heterogeneity in prognosis and therapeutic response.We sought to construct a machine-learning prognosticmodel based on a complement-related risk signature(CRRS)and to situate this signature within the CRC immune microenvironment.Methods:Transcriptomic profiles with matched clinical annotations from TCGA and GEO CRC cohorts were analyzed.Prognostic CRRS genes were screened using Cox proportional hazards modeling alongside machine-learning procedures.A random survival forest(RSF)predictor was trained and externally validated.Comparisons of immune infiltration,mutational burden,pathway enrichment,and drug sensitivity were made between risk groups.The function of FAM84A,a key model gene,was examined in CRC cell lines.Results:The six-gene CRRS model accurately stratified patients by survival outcomes.Low-risk patients exhibited greater immune cell infiltration and higher predicted response to immunotherapy and chemotherapy,while high-risk patients showed enrichment of complement activation and matrix remodeling pathways.FAM84A was shown to promote CRC cell proliferation,migration,and epithelial–mesenchymal transition.Conclusion:CRRS is a critical modulator of the CRC immune microenvironment.The developed model enables precise risk prediction and supports individualized therapeutic decisions in CRC.
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Supported by National Natural Science Foundation of China,No.81972255,No.81772597 and No.81672412Guangdong Natural Science Foundation,No.2017A030311002+4 种基金Guangdong Science and Technology Foundation,No.2017A020215196Fundamental Research Funds for the Central Universities of Sun YatSen University,No.17ykpy44Science Foundation of Jiangxi,No.20181BAB214002Education Department Science and Technology Foundation of Jiangxi,No.GJJ170936Grant from Guangdong Science and Technology Department,No.2017B030314026
文摘BACKGROUND Hepatocellular carcinoma(HCC)is a common cancer with a poor prognosis.Previous studies revealed that the tumor microenvironment(TME)plays an important role in HCC progression,recurrence,and metastasis,leading to poor prognosis.However,the effects of genes involved in TME on the prognosis of HCC patients remain unclear.Here,we investigated the HCC microenvironment to identify prognostic genes for HCC.AIM To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC.METHODS We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm.Additionally,a risk score model was established based on Differentially Expressed Genes(DEGs)between high and lowimmune/stromal score patients.RESULTS The risk score model consisting of eight genes was constructed and validated in the HCC patients.The patients were divided into high-or low-risk groups.The genes(Disabled homolog 2,Musculin,C-X-C motif chemokine ligand 8,Galectin 3,B-cell-activating transcription factor,Killer cell lectin like receptor B1,Endoglin and adenomatosis polyposis coli tumor suppressor)involved in our risk score model were considered to be potential immunotherapy targets,and they may provide better performance in combination.Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway,respectively,related to the immune-related genes in the DEGs between high-and low-risk groups.The receiver operating characteristic(ROC)curve analysis confirmed the good potency of the risk score prognostic model.Moreover,we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database.A nomogram was established to predict the overall survival of HCC patients.CONCLUSION The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.
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This study was supported by a grant from Tianjin Key Medical Discipline(Specialty)Construction Project.
文摘Background:Due to the high heterogeneity among hepatocellular carcinoma(HCC)patients receiving transarterial chemoembolization(TACE),the prognosis of patients varies significantly.The decisionmaking on the initiation and/or repetition of TACE under different liver functions is a matter of concern in clinical practice.Thus,we aimed to develop a prediction model for TACE candidates using risk stratification based on varied liver function.Methods:A total of 222 unresectable HCC patients who underwent TACE as their only treatment were included in this study.Cox proportional hazards regression was performed to select the independent risk factors and establish a predictive model for the overall survival(OS).The model was validated in patients with different Child-Pugh class and compared to previous TACE scoring systems.Results:The five independent risk factors,including alpha-fetoprotein(AFP)level,maximal tumor size,the increase of albumin-bilirubin(ALBI)grade score,tumor response,and the increase of aspartate aminotransferase(AST),were used to build a prognostic model(ASARA).In the training and validation cohorts,the OS of patients with ASARA score≤2 was significantly higher than that of patients with ASARA score>2(P<0.001,P=0.006,respectively).The ASARA model and its modified version“AS(ARA)”can effectively distinguish the OS(P<0.001,P=0.004)between patients with Child-Pugh class A and B,and the C-index was 0.687 and 0.706,respectively.For repeated TACE,the ASARA model was superior to Assessment for Retreatment with TACE(ART)and ALBI grade,maximal tumor size,AFP,and tumor response(ASAR)among Child-Pugh class A patients.For the first TACE,the performance of AS(ARA)was better than that of modified hepatoma arterial-embolization prognostic(mHAP),mHAP3,and ASA(R)models among Child-Pugh class B patients.Conclusions:The ASARA scoring system is valuable in the decision-making of TACE repetition for HCC patients,especially Child-Pugh class A patients.The modified AS(ARA)can be used to screen the ideal candidate for TACE initiation in Child-Pugh class B patients with poor liver function.
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 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.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 Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 60%of GC are linked to infection with Helicobacter pylori(H.pylori),a gram-negative,active,microaerophilic,and helical bacterium.This parasite induces GC by producing toxic factors,such as cytotoxin-related gene A,vacuolar cytotoxin A,and outer membrane proteins.Ferroptosis,or iron-dependent programmed cell death,has been linked to GC,although there has been little research on the link between H.pylori infection-related GC and ferroptosis.AIM To identify coregulated differentially expressed genes among ferroptosis-related genes(FRGs)in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.METHODS Gene expression profiles of GC patients and those with H.pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus(GEO)databases.The FRGs were acquired from the FerrDb database.A ferroptosis-related gene prognostic index(FRGPI)was created using least absolute shrinkage and selection operator–Cox regression.The predictive ability of the FRGPI was validated in the GEO cohort.Finally,we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.RESULTS Four hub genes were identified(NOX4,MTCH1,GABARAPL2,and SLC2A3)and shown to accurately predict GC and H.pylori-associated GC.The FRGPI based on the hub genes could independently predict GC patient survival;GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group.The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression.Moreover,the gene expression levels of common immune checkpoint proteins dramatically increased in the highrisk subgroup of the FRGPI cohort.The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane.The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.CONCLUSION In this study,we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.
