Prognostic model for esophagogastric variceal rebleeding after endoscopic treatment in liver cirrhosis: A Chinese multicenter study

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.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment

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.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

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.

Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases

BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis,thereby enhancing the ability to evaluate patient outcomes.AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis,thereby enhancing patient outcome assessment.METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM(type III),admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018.The dataset was divided into a development cohort and validation cohort in a ratio of 2:1.In the development cohort,we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients.Subsequently,we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis,calibration curves,and clinical decision curves.A nomogram was created to visually represent the prediction model,which was then externally validated using the validation cohort.RESULTS A total of 372 patients were included in this study,comprising 248 individuals in the development cohort and 124 individuals in the validation cohort.Based on Cox analysis results,our final prediction model incorporated five independent risk factors including albumin levels,primary tumor size,presence of extrahepatic metastases,surgical treatment status,and chemotherapy administration.The 1-,3-,and 5-years Area Under the Curve values in the development cohort are 0.753,0.859,and 0.909,respectively;whereas in the validation cohort,they are observed to be 0.772,0.848,and 0.923.Furthermore,the calibration curves demonstrated excellent consistency between observed values and actual values.Finally,the decision curve analysis curve indicated substantial net clinical benefit.CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model,demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.

Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma

Background:Cytotoxic T lymphocytes(CD8+T)cells function critically in mediating anti-tumor immune response in cancer patients.Characterizing the specific functions of CD8+T cells in lung adenocarcinoma(LUAD)could help better understand local anti-tumor immune responses and estimate the effect of immunotherapy.Methods:Gens related to CD8+T cells were identified by cluster analysis based on the single-cell sequencing data of three LUAD tissues and their paired normal tissues.Weighted gene co-expression network analysis(WGCNA),consensus clustering,differential expression analysis,least absolute shrinkage and selection operator(LASSO)and Cox regression analysis were conducted to classify molecular subtypes for LUAD and to develop a risk model using prognostic genes related to CD8+T cells.Expression of the genes in the prognostic model,their effects on tumor cell invasion,and interactions with CD8+T cells were verified by cell experiments.Results:This study defined two LUAD clusters(CD8+0 and CD8+1)based on CD8+T cells,with cluster CD8+0 being significantly associated with the prognosis of LUAD.Three heterogeneous subtypes(clusters 1,2,and 3)differing in prognosis,genome mutation events,and immune status were categorized using 42 prognostic genes.A prognostic model created based on 11 significant genes(including CD200R1,CLEC17A,ZC3H12D,GNG7,SNX30,CDCP1,NEIL3,IGF2BP1,RHOV,ABCC2,and KRT81)was able to independently estimate the death risk for patients in different LUAD cohorts.Moreover,the model also showed general applicability in external validation cohorts.Low-risk patients could benefit more from taking immunotherapy and were significantly related to the resistance to anticancer drugs.The results from cell experiments demonstrated that the expression of CD200R1,CLEC17A,ZC3H12D,GNG7,and SNX30 was significantly downregulated,while that of CDCP1,NEIL3,IGF2BP1,RHOV,ABCC2 and KRT81 was upregulated in LUAD cells.Inhibition of CD200R1 greatly increased the invasiveness of the LUAD cells,but inhibiting CDCP1 expression weakened the invasion ability of LUAD cells.Conclusion:This study defined two prognostic CD8+T cell clusters and classified three heterogeneous molecular subtypes for LUAD.A prognostic model predictive of the potential effects of immunotherapy on LUAD patients was developed.

A Prognostic Model Based on Colony Stimulating Factors-related Genes in Triple-negative Breast Cancer

Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC.

Prognostic prediction models for postoperative patients with stageⅠtoⅢcolorectal cancer based on machine learning

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.

Elucidating the molecular basis of ATP-induced cell death in breast cancer: Construction of a robust prognostic model

BACKGROUND Breast cancer is a multifaceted and formidable disease with profound public health implications.Cell demise mechanisms play a pivotal role in breast cancer pathogenesis,with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic pertinence.AIM To investigate the impact of ATP-induced cell death(AICD)on breast cancer,enhancing our understanding of its mechanism.METHODS The foundational genes orchestrating AICD mechanisms were extracted from the literature,underpinning the establishment of a prognostic model.Simultaneously,a microRNA(miRNA)prognostic model was constructed that mirrored the gene-based prognostic model.Distinctions between high-and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized,with the aim of delineating common influence mechanisms,substantiated through enrichment analysis and immune infiltration assessment.RESULTS The mRNA prognostic model in this study encompassed four specific mRNAs:P2X purinoceptor 4,pannexin 1,caspase 7,and cyclin 2.The miRNA prognostic model integrated four pivotal miRNAs:hsa-miR-615-3p,hsa-miR-519b-3p,hsa-miR-342-3p,and hsa-miR-324-3p.B cells,CD4+T cells,CD8+T cells,endothelial cells,and macrophages exhibited inverse correlations with risk scores across all breast cancer subtypes.Furthermore,Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways,while miRNA risk scores significantly enriched 29 signaling pathways,with 16 pathways being jointly enriched.CONCLUSION Of paramount significance,distinct mRNA and miRNA signature models were devised tailored to AICD,both potentially autonomous prognostic factors.This study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools,offering an unparalleled window for innovative interventions.Essentially,this paper reveals the hitherto enigmatic link between AICD and breast cancer,potentially leading to revolutionary progress in personalized oncology.

Evaluation of Serum Anti-Müllerian Hormone (AMH) Values for 28,016 Bulgarian Women: Prognostic Statistical Model of Age Specific AMH Declining

The present study aims to establish a relationship between serum AMH levels and age in a large group of women living in Bulgaria, as well as to establish reference age-specific AMH levels in women that would serve as an initial estimate of ovarian age. A total of 28,016 women on the territory of the Republic of Bulgaria were tested for serum AMH levels with a median age of 37.0 years (interquartile range 32.0 to 41.0). For women aged 20 - 29 years, the Bulgarian population has relatively high median levels of AMH, similar to women of Asian origin. For women aged 30 - 34 years, our results are comparable to those of women living in Western Europe. For women aged 35 - 39 years, our results are comparable to those of women living in the territory of India and Kenya. For women aged 40 - 44 years, our results were lower than those for women from the Western European and Chinese populations, close to the Indian and higher than Korean and Kenya populations, respectively. Our results for women of Bulgarian origin are also comparable to US Latina women at age 30, 35 and 40 ages. On the base on constructed a statistical model to predicting the decline in AMH levels at different ages, we found non-linear structure of AMH decline for the low AMH 3.5) the dependence of the decline of AMH on age was confirmed as linear. In conclusion, we evaluated the serum level of AMH in Bulgarian women and established age-specific AMH percentile reference values based on a large representative sample. We have developed a prognostic statistical model that can facilitate the application of AMH in clinical practice and the prediction of reproductive capacity and population health.

A model of five genes of tumor microenvironment predicts prognosis in Cholangiocarcinoma

Background:Cholangiocarcinoma(CCA)is highly malignant and has a poor prognosis has a high malignant degree and poor prognosis.The purpose of this study is to develop a new prognostic model based on genes related to the tumor microenvironment(TME).Methods:Derived from the discerned differentially expressed genes within The Cancer Genome Atlas(TCGA)dataset,this investigation employed the methodology of weighted gene co-expression network analysis(WGCNA)to ascertain gene co-expressed modules intricately linked to the Tumor Microenvironment(TME)among Cholangiocarcinoma(CCA)patients.The genes associated with prognosis,as identified through Cox regression analysis,were employed in the formulation of a predictive model.This model underwent validation,leading to the development of a risk score formula and nomogram.Concurrently,we validated the model’s reliability using data from CCA patients in the Gene Expression Omnibus(GEO)database(accession:GSE107943).Results:6139 DEGs were divided into 10 co-expressed gene modules using WGCNA.Among these,two modules(blue module with 832 genes and brown module with 1379 genes)showed high correlation with the TME.Five prognostic genes(BNIP3,COL4A3,SPRED3,CEBPB,PLOD2)were identified through Cox regression analysis,and a prognostic model and risk score formula were developed based on these genes.Risk score formula:Risk score=BNIP3×1.70520-COL4A3×2.39815+SPRED3×1.17936+CEBPB×0.40456+PLOD2×0.24785.Kaplan-Meier survival analysis revealed that the survival probabilities of the low-risk group were significantly higher than those of the high-risk group.Furthermore,the related evaluation indexes suggested that the model exhibited strong predictive ability.Conclusion:The prognostic model,based on five TME-related genes(BNIP3,COL4A3,SPRED3,CEBPB,PLOD2),could accurately assess the prognosis of CCA patients to aid in guiding clinical decisions.

Prognostic factors for chronic severe hepatitis and construction of a prognostic model

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.

Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment

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.

ASARA,a prediction model based on Child-Pugh class in hepatocellular carcinoma patients undergoing transarterial chemoembolization

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.

Construction and Verification of an RNA-Binding Protein-Associated Prognostic Model for Gliomas

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.

Construction of a clinical survival prognostic model for middle-aged and elderly patients with stage III rectal adenocarcinoma

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.

Prognostic model of hepatocellular carcinoma based on cancer grade

BACKGROUND Hepatocellular carcinoma(HCC)is the most common type of primary liver cancer.With highly invasive biological characteristics and a lack of obvious clinical manifestations,HCC usually has a poor prognosis and ranks fourth in cancer mortality.The aetiology and exact molecular mechanism of primary HCC are still unclear.AIM To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.METHODS By comparing the gene expression levels of patients with different cancer grades of HCC,we screened out differentially expressed genes associated with tumour grade.By protein-protein interaction(PPI)network analysis,we obtained the top 2 PPI networks and hub genes from these differentially expressed genes.By using least absolute shrinkage and selection operator Cox regression,13 prognostic genes were selected for feature extraction,and a prognostic risk model of HCC was established.RESULTS The model had significant prognostic ability in HCC.We also analysed the biological functions of these prognostic genes.CONCLUSION By comparing the gene profiles of patients with different stages of HCC,We have constructed a prognosis model consisting of 13 genes that have important prognostic value.This model has good application value and can be explained clinically.

Development of preoperative prognostic models including radiological features for survival of singular nodular HCC patients

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.

Development of a prognostic model for one-year surgery risk in Crohn’s disease patients: A retrospective study

BACKGROUND Accelerated therapeutic treatment should be considered in patients with progressive Crohn’s disease(CD)to prevent complications as well as surgery.Therefore,screening for risk factors and predicting the need for early surgery are of great importance in clinical practice.AIM To establish a model to predict CD-related early surgery.METHODS This was a retrospective study collecting data from CD patients diagnosed at our inflammatory bowel disease center from January 1,2012 to December 31,2016.All data were randomly stratified into a training set and a testing set at a ratio of 8:2.Multivariable logistic regression analysis was conducted with receiver operating characteristic curves constructed and areas under the curve calculated.This model was further validated with calibration and discrimination estimated.A nomogram was finally developed.RESULTS A total of 1002 eligible patients were enrolled with a mean follow-up period of 53.54±13.10 mo.In total,24.25%of patients received intestinal surgery within 1 year after diagnosis due to complications or disease relapse.Disease behavior(B2:OR[odds ratio]=6.693,P<0.001;B3:OR=14.405,P<0.001),smoking(OR=4.135,P<0.001),body mass index(OR=0.873,P<0.001)and C-reactive protein(OR=1.022,P=0.001)at diagnosis,previous perianal(OR=9.483,P<0.001)or intestinal surgery(OR=8.887,P<0.001),maximum bowel wall thickness(OR=1.965,P<0.001),use of biologics(OR=0.264,P<0.001),and exclusive enteral nutrition(OR=0.089,P<0.001)were identified as independent significant factors associated with early intestinal surgery.A prognostic model was established and further validated.The receiver operating characteristic curves and calculated areas under the curves(94.7%)confirmed an ideal predictive ability of this model with a sensitivity of 75.92%and specificity of 95.81%.A nomogram was developed to simplify the use of the predictive model in clinical practice.CONCLUSION This prognostic model can effectively predict 1-year risk of CD-related intestinal surgery,which will assist in screening progressive CD patients and tailoring therapeutic management.

An inflammatory-related genes signature based model for prognosis prediction in breast cancer

Background:Breast cancer has become the most common malignant tumor in the world.It is vital to discover novel prognostic biomarkers despite the fact that the majority of breast cancer patients have a good prognosis because of the high heterogeneity of breast cancer,which causes the disparity in prognosis.Recently,inflammatory-related genes have been proven to play an important role in the development and progression of breast cancer,so we set out to investigate the predictive usefulness of inflammatory-related genes in breast malignancies.Methods:We assessed the connection between Inflammatory-Related Genes(IRGs)and breast cancer by studying the TCGA database.Following differential and univariate Cox regression analysis,prognosis-related differentially expressed inflammatory genes were estimated.The prognostic model was constructed through the Least Absolute Shrinkage and Selector Operation(LASSO)regression based on the IRGs.The accuracy of the prognostic model was then evaluated using the Kaplan-Meier and Receiver Operating Characteristic(ROC)curves.The nomogram model was established to predict the survival rate of breast cancer patients clinically.Based on the prognostic expression,we also looked at immune cell infiltration and the function of immune-related pathways.The CellMiner database was used to research drug sensitivity.Results:In this study,7 IRGs were selected to construct a prognostic risk model.Further research revealed a negative relationship between the risk score and the prognosis of breast cancer patients.The ROC curve proved the accuracy of the prognostic model,and the nomogram accurately predicted survival rate.The scores of tumorinfiltrating immune cells and immune-related pathways were utilized to calculate the differences between the low-and high-risk groups,and then explored the relationship between drug susceptibility and the genes that were included in the model.Conclusion:These findings contributed to a better understanding of the function of inflammatory-related genes in breast cancer,and the prognostic risk model provides a potentially promising prognostic strategy for breast cancer.