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.

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.

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.

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.

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 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.

Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.

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.

Construction and validation of prognostic model of Cuproptosis-related LncRNA in osteosarcoma

Cuproptosis is a newly discovered form of apoptotic process that is thought to play an important role in cancer therapy.Long non-coding RNA(lncRNA)is involved in regulating many physiological and pathological activities of cells.The aim of this study was to investigate the prognostic significance of Cuproptosis-associated lncRNAs in osteosarcoma.Methods:The Gene expression profiling of osteosarcoma samples versus normal samples and corresponding clinical data were downloaded from the public databases UCSC Xena and GTEx,and the cuproptosis gene was obtained from the published literature,the prognostic model of osteosarcoma cuproptosis-related lncRNA was constructed by using coexpression network,minimum absolute contraction and selection algorithm(LASSO)and Cox regression model.Receiver operating characteristic(ROC)curves and nomograms were used to assess the predictive power of the model.Single-sample gene set enrichment analysis(ssGSEA)was used to explore the relationship between osteosarcoma immune cells and function in different risk groups.Results:181 cuproptosis-related lncRNAs were obtained by co-expression analysis of 19 cuproptosis genes collected.Ten lncRNAs were screened out by differential analysis and single-factor Cox analysis.Three cuproptosis-related lncrnas(AC124798.1,AC090152.1,AC090559.1)were screened by Lasso and multivariate Cox regression to construct the prognostic model.Patients were divided into high and low risk groups based on the median risk score.The results of overall survival,risk score distribution and survival status in the lowrisk group were better than those in the high-risk group,and were verified in the internal data.Univariate and multivariate Cox regression analyses showed that risk score was an independent prognostic factor.Nomograms and ROC curves showed that the prognostic model had good predictive ability.The results of ssGSEA suggest that immune cells and function may be inhibited in the high-risk group.Conclusion:The 3 cuproptosis-related lncRNAs may be helpful to guide the prognosis of osteosarcoma patients and provide some theoretical basis for clinical decision.

Prognostic model and treatment plan analysis of hepatocellular carcinoma based on genes related to glutamine metabolism

Objective:To identify the prognosis of hepatocellular carcinoma(HCC)and the effect of anti-cancer drug therapy by screening glutamine metabolism-related signature genes because glutamine metabolism plays an important role in tumor development.Methods:We obtained gene expression samples of normal liver tissue and hepatocellular carcinoma from the TCGA database and GEO database,screened for differentially expressed glutamine metabolismrelated genes(GMRGs),constructed a prognostic model by lasso regression and step cox analysis,and assessed the differences in drug sensitivity between high-and low-risk groups.Results:We screened 23 differentially expressed GMRGs by differential analysis,and correlation loop plots and PPI protein interaction networks indicated that these differential genes were strongly correlated.The four most characterized genes(CAD,PPAT,PYCR3,and SLC7A11)were obtained by lasso regression and step cox,and a risk model was constructed and confirmed to have reliable predictive power in the TCGA dataset and GEO dataset.Finally,immunotherapy is better in the high-risk group than in the low-risk group,and chemotherapy and targeted drug therapy are better in the low-risk group than in the high-risk group.Conclusion:In conclusion,we have developed a reliable prognostic risk model characterized by glutamine metabolism-related genes,which may provide a viable basis for the prognosis and Treatment options of HCC patients.

Construction and validation of an immune-related lncRNA prognostic model for rectal adenocarcinomas

Objective This study aimed to construct a prognostic model for rectal adenocarcinomas based on immune-related long noncoding RNAs(lncRNAs)and verify its prediction efficiency.Methods Transcript data and clinical data of rectal adenocarcinomas were downloaded from The Cancer Genome Atlas(TCGA)database.Perl software(strawberry version)and R language(version 3.6.1)were used to analyze the immune-related genes and immune-related lncRNAs of rectal adenocarcinomas,and the differentially expressed immune-related lncRNAs were screened according to the criteria|log2FC|>1 and P<0.05.The key immune-related lncRNAs were screened using single-factor Cox regression analysis and lasso regression analysis.Multivariate Cox regression analysis was performed to construct an immune-related lncRNA prognostic model using the risk scores.Next,we evaluated the effectiveness of the model through Kaplan-Meier(K-M)survival analysis,ROC curve analysis,and independent prognostic analysis of clinical features.In addition,prognostic biomarkers of immune-related lncRNAs in the model were analyzed by K-M survival analysis.Results In this study,we obtained gene expression profile matrices of 89 rectal adenocarcinomas and 2 paracancerous specimens from TCGA database and applied immunologic signatures to these transcripts.Through R and Perl software analysis,we obtained 847 immune-related lncRNAs and 331 protein-encoded immune-related genes in rectal adenocarcinomas.Eight important immune-related lncRNAs related to the prognosis of rectal adenocarcinomas were identified using univariate Cox regression and lasso regression analysis.Furthermore,four immune-related lncRNAs were identified as prognostic markers of rectal adenocarcinomas via multivariate Cox regression analysis.The prognostic risk model was as follows:risk score=(-4.084)*expression LINC01871+(3.112)*expression AL158152.2+(7.616)*expression PXN-AS1+(-0.867)*expression HCP5.The independent prognostic effect of the rectal adenocarcinoma risk score model was revealed through K-M analysis,ROC curve analysis,and univariate,and multivariate Cox regression analysis(P=0.035).LINC01871(P=0.006),PXN-AS1(P=0.008),and AL158152.2(P=0.0386)were closely correlated with the prognosis of rectal adenocarcinomas through the K-M survival analysis.Conclusion We constructed a prognostic model of rectal adenocarcinomas based on four immune-related lncRNAs by analyzing the data based on TCGA database,with high prediction accuracy.We also identified two biomarkers with poor prognosis(PXN-AS1 and AL158152.2)and one biomarker with good prognosis(LINC01871).

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.

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.

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.

Development and Validation of a Prognostic Model for One-year Survival of Cirrhosis Patients with First-ever Spontaneous Bacterial Peritonitis

Background and Aims: Spontaneous bacterial peritonitis(SBP) is one of the leading causes of death in patients withliver cirrhosis. We aimed to establish a prognostic model toevaluate the 1-year survival of cirrhosis patients after thefirst episode of SBP. Methods: A prognostic model was developedbased on a retrospective derivation cohort of 309cirrhosis patients with first-ever SBP and was validated in aseparate validation cohort of 141 patients. We used Uno’sconcordance, calibration curve, and decision curve (DCA)analysis to evaluate the discrimination, calibration, and clinicalnet benefit of the model. Results: A total of 59 (19.1%)patients in the derivation cohort and 42 (29.8%) patientsin the validation cohort died over the course of 1 year. Aprognostic model in nomogram form was developed withpredictors including age [hazard ratio (HR): 1.25;95% confidenceinterval (CI): 0.92–1.71], total serum bilirubin (HR:1.66;95% CI: 1.28–2.14), serum sodium (HR: 0.94;95%CI: 0.90–0.98), history of hypertension (HR: 2.52;95% CI:1.44–4.41) and hepatic encephalopathy (HR: 2.06;95%CI: 1.13–3.73). The nomogram had a higher concordance(0.79) compared with the model end-stage liver disease(0.67) or Child-Turcotte-Pugh (0.71) score. The nomogramalso showed acceptable calibration (calibration slope, 1.12;Bier score, 0.15±0.21) and optimal clinical net benefit in thevalidation cohort. Conclusions: This prediction model developedbased on characteristics of first-ever SBP patientsmay benefit the prediction of patients’ 1-year survival.

A New Prognostic Model Covering All Stages of Intrahepatic Cholangiocarcinoma

Background and Aims:Intrahepatic cholangiocarcinoma(ICC)is the second most common primary hepatic malignancy that causes a poor survival.We aimed to identify its prognostic factors and to develop a nomogram that will predict survival of ICC patients among all stages.Methods:A total of 442 patients with pathology-proven ICC registered at the Fifth Medical Center of PLA General Hospital between July 2007 and December 2019 were enrolled.Subjects were followed for survival status until June 30,2020.A prognostic model visualized as a nomogram was constructed in the training cohort using multivariate cox model,and was then validated in the validation cohort.Results:The median age was 55 years.With a median follow-up of 50.4 months,337 patients died.The median survival was 11.6 months,with 1-,3-and 5-year survival rates of 48.3%,22.7%and 16.2%,respectively.Factors associated with overall survival were multiple tumors,lymph node involvement,vascular invasion,distant metastasis,decreased albumin,elevated lactate dehydrogenase(LDH),decreased iron,elevated fi-brinogen,elevated CA125 and elevated CA19-9.A nomo-gram predicting survival of ICC patients at the time of di-agnosis achieved a Harrel’s c-statistic of 0.758,significantly higher than the 0.582 of the TNM stage alone.Predicted median survivals of those within the low,mid and high-risk subgroups were 35.6,12.1 and 6.2 months,respectively.Conclusions:A nomogram based on imaging data and serum biomarkers at diagnosis showed good ability to predict survival in patients with all stages of ICC.Further studies are needed to validate the prognostic capability of our new model.