Why MUC16 mutations lead to a better prognosis:A study based on The Cancer Genome Atlas gastric cancer cohort

BACKGROUND MUC16,encoding cancer antigen 125,is a frequently mutated gene in gastric cancer.In addition,MUC16 mutations seem to result in a better prognosis in gastric cancer.However,the mechanisms that lead to a better prognosis by MUC16 mutations have not yet been clarified.AIM To delve deeper into the underlying mechanisms that explain why MUC16 mutations signal a better prognosis in gastric cancer.METHODS We used multi-omics data,including mRNA,simple nucleotide variation,copy number variation and methylation data from The Cancer Genome Atlas,to explore the relationship between MUC16 mutations and prognosis.Cox regression and random survival forest algorithms were applied to search for hub genes.Gene set enrichment analysis was used to elucidate the molecular mechanisms.Single-sample gene set enrichment analysis and“EpiDISH”were used to assess immune cells infiltration,and“ESTIMATE”for analysis of the tumor microenvironment.RESULTS Our study found that compared to the wild-type group,the mutation group had a better prognosis.Additional analysis indicated that the MUC16 mutations appear to activate the DNA repair and p53 pathways to act as an anti-tumor agent.We also identified a key gene,NPY1R(neuropeptide Y receptor Y1),which was significantly more highly expressed in the MUC16 mutations group than in the MUC16 wild-type group.The high expression of NPY1R predicted a poorer prognosis,which was also confirmed in a separate Gene Expression Omnibus cohort.Further susceptibility analysis revealed that NPY1R might be a potential drug target for gastric cancer.Furthermore,in the analysis of the tumor microenvironment,we found that immune cells in the mutation group exhibited higher anti-tumor effects.In addition,the tumor mutation burden and cancer stem cells index were also higher in the mutation group than in the wild-type group.CONCLUSION We speculated that the MUC16 mutations might activate the p53 pathway and DNA repair pathway:alternatively,the tumor microenvironment may be involved.

Systematic analysis of DNA polymerases as therapeutic targets in pan-cancers

Introduction:DNA polymerases are crucial for maintaining genome stability and influencing tumorigenesis.However,the clinical implications of DNA polymerases in tumorigenesis and their potential as anti-cancer therapy targets are not well understood.Methods:We conducted a systematic analysis using TCGA Pan-Cancer Atlas data and Gene Set Cancer Analysis results to examine the expression profiles of 15 DNA polymerases(POLYs)and their clinical correlations.We also evaluated the prognostic value of POLYs by analyzing their expression levels in relation to overall survival time(OS)using Kaplan-Meier survival curves.Additionally,we investigated the correlations between POLY expression and immune cells,DNA damage repair(DDR)pathways,and ubiquitination.Drug sensitivity analysis was performed to assess the relationship between POLY expression and drug response.Results:Our analysis revealed that 14 out of 15 POLYs exhibited significantly distinct expression patterns between tumor and normal samples across most cancer types,except for DNA nucleotidylexotransferase(DNTT).Specifically,POLD1 and POLE showed elevated expression in almost all cancers,while POLQ exhibited high expression levels in all cancer types.Some POLYs showed heightened expression in specific cancer subtypes,while others exhibited low expression.Kaplan-Meier survival curves demonstrated significant prognostic value of POLYs in multiple cancers,including PAAD,KIRC,and ACC.Cox analysis further validated these findings.Alteration patterns of POLYs varied significantly among different cancer types and were associated with poorer survival outcomes.Significant correlations were observed between the expression of POLY members and immune cells,DDR pathways,and ubiquitination.Drug sensitivity analysis indicated an inverse relationship between POLY expression and drug response.Conclusion:Our comprehensive study highlights the significant role of POLYs in cancer development and identifies them as promising prognostic and immunological biomarkers for various cancer types.Additionally,targeting POLYs therapeutically holds promise for tumor immunotherapy.

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer

BACKGROUND Breast cancer(BC),a leading malignant disease,affects women all over the world.Cancer associated fibroblasts(CAFs)stimulate epithelial-mesenchymal transition,and induce chemoresistance and immunosuppression.AIM To establish a CAFs-associated prognostic signature to improve BC patient out-come estimation.METHODS We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas(TCGA)databases,and 3661 BC samples from the The Gene Expression Omnibus.CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm.CAF-associated gene identification was done by weighted gene co-expression network analysis.A CAF risk signature was established via univariate,least absolute shrinkage and selection operator regression,and mul-tivariate Cox regression analyses.The receiver operating characteristic(ROC)and Kaplan-Meier curves were employed to evaluate the predictability of the model.Subsequently,a nomogram was developed with the risk score and patient clinical signature.Using Spearman's correlations analysis,the relationship between CAF risk score and gene set enrichment scores were examined.Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction(qRT-PCR).RESULTS Employing an 8-gene(IL18,MYD88,GLIPR1,TNN,BHLHE41,DNAJB5,FKBP14,and XG)signature,we attemp-ted to estimate BC patient prognosis.Based on our analysis,high-risk patients exhibited worse outcomes than low-risk patients.Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis.ROC analysis exhibited satisfactory nomogram predictability.The area under the curve showed 0.805 at 3 years,and 0.801 at 5 years in the TCGA cohort.We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes.CAF risk score was significantly correlated with most hallmark gene sets.Finally,the prognostic signature were further validated by qRT-PCR.CONCLUSION We introduced a newly-discovered CAFs-associated gene signature,which can be employed to estimate BC patient outcomes conveniently and accurately.

Emerging molecular classifications and therapeutic implications for gastric cancer

Gastric cancer(GC) is a highly aggressive and life-threatening malignancy.Even with radical surgical removal and front-line chemotherapy,more than half of GCs locally relapse and metastasize at a distant site.The dismal outcomes reflect the ineffectiveness of a one-size fits-all approach for a highly heterogeneous disease with diverse etiological causes and complex molecular underpinnings.The recent comprehensive genomic and molecular profiling has led to our deepened understanding of GC.The emerging molecular classification schemes based on the genetic,epigenetic,and molecular signatures are providing great promise for the development of more effective therapeutic strategies in a more personalized and precise manner.To this end,the Cancer Genome Atlas(TCGA) research network conducted a comprehensive molecular evaluation of primary GCs and proposed a new molecular classification dividing GCs into four subtypes:Epstein-Barr virus-associated tumors,microsatellite unstable tumors,genomically stable tumors,and tumors with chromosomal instability.This review primarily focuses on the TCGA molecular classification of GCs and discusses the implications on novel targeted therapy strategies.We believe that these fundamental findings will support the future application of targeted therapies and will guide our efforts to develop more efficacious drugs to treat human GCs.

Weighted Gene Co-expression Network Analysis of Gene Modules for the Prognosis of Esophageal Cancer

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas(TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival(PFS) or overall survival(OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that 'glycoprotein binding' was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor(PTAFR) and feline Gardner-Rasheed(FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Nine-long non-coding ribonucleic acid signature can improve the survival prediction of colorectal cancer

BACKGROUND Investigating molecular biomarkers that accurately predict prognosis is of considerable clinical significance.Accumulating evidence suggests that long noncoding ribonucleic acids(lncRNAs)are frequently aberrantly expressed in colorectal cancer(CRC).AIM To elucidate the prognostic function of multiple lncRNAs serving as biomarkers in CRC.METHODS We performed lncRNA expression profiling using the lncRNA mining approach in large CRC cohorts from The Cancer Genome Atlas(TCGA)database.Receiver operating characteristic analysis was performed to identify the optimal cutoff point at which patients could be classified into the high-risk or low-risk groups.Based on the Cox coefficient of the individual lncRNAs,we identified a ninelncRNA signature that was associated with the survival of CRC patients in the training set(n=175).The prognostic value of this nine-lncRNA signature was validated in the testing set(n=174)and TCGA set(n=349).The prognostic models,consisting of these nine CRC-specific lncRNAs,performed well for risk stratification in the testing set and TCGA set.Time-dependent receiver operating characteristic analysis indicated that this predictive model had good performance.RESULTS Multivariate Cox regression and stratification analysis demonstrated that this nine-lncRNA signature was independent of other clinical features in predicting overall survival.Functional enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology terms further indicated that these nine prognostic lncRNAs were closely associated with carcinogenesis-associated pathways and biological functions in CRC.CONCLUSION A nine-lncRNA expression signature was identified and validated that could improve the prognosis prediction of CRC,thereby providing potential prognostic biomarkers and efficient therapeutic targets for patients with CRC.

Identification of potential diagnostic and prognostic biomarkers for breast cancer based on gene expression omnibus

BACKGROUND Breast cancer is regarded as a highly malignant neoplasm in the female population,posing a significant risk to women’s overall well-being.The prevalence of breast cancer has been observed to rise in China,accompanied by an earlier age of onset when compared to Western countries.Breast cancer continues to be a prominent contributor to cancer-related mortality and morbidity among women,primarily due to its limited responsiveness to conventional treatment modalities.The diagnostic process is challenging due to the presence of non-specific clinical manifestations and the suboptimal precision of conventional diagnostic tests.There is a prevailing uncertainty regarding the most effective screening method and target populations,as well as the specificities and execution of screening programs.AIM To identify diagnostic and prognostic biomarkers for breast cancer.METHODS Overlapping differentially expressed genes were screened based on Gene Expression Omnibus(GSE36765,GSE10810,and GSE20086)and The Cancer Genome Atlas datasets.A protein-protein interaction network was applied to excavate the hub genes among these differentially expressed genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses,as well as gene set enrichment analyses,were conducted to examine the functions of these genes and their potential mechanisms in the development of breast cancer.For clarification of the diagnostic and prognostic roles of these genes,Kaplan–Mei-er and Cox proportional hazards analyses were conducted.RESULTS This study demonstrated that calreticulin,heat shock protein family B member 1,insulin-like growth Factor 1,interleukin-1 receptor 1,Krüppel-like factor 4,suppressor of cytokine signaling 3,and triosephosphate isomerase 1 are potential diagnostic biomarkers of breast cancer as well as potential treatment targets with clinical implications.CONCLUSION The screening of biomarkers is of guiding significance for the diagnosis and prognosis of the diseases.

Comprehensive analysis of distal-less homeobox family gene expression in colon cancer

BACKGROUND The distal-less homeobox(DLX)gene family plays an important role in the development of several tumors.However,the expression pattern,prognostic and diagnostic value,possible regulatory mechanisms,and the relationship between DLX family genes and immune infiltration in colon cancer have not been systematically reported.AIM We aimed to comprehensively analyze the biological role of the DLX gene family in the pathogenesis of colon cancer.METHODS Colon cancer tissue and normal colon tissue samples were collected from the Cancer Genome Atlas and Gene Expression Omnibus databases.Wilcoxon rank sum test and t-test were used to assess DLX gene family expression between colon cancer tissue and unpaired normal colon tissue.cBioPortal was used to analyze DLX gene family variants.R software was used to analyze DLX gene expression in colon cancer and the relationship between DLX gene family expression and clinical features and correlation heat map.The survival package and Cox regression module were used to assess the prognostic value of the DLX gene family.The pROC package was used to analyze the diagnostic value of the DLX gene family.R software was used to analyze the possible regulatory mechanisms of DLX gene family members and related genes.The GSVA package was used to analyze the relationship between the DLX gene family and immune infiltration.The ggplot2,the survminer package,and the clusterProfiler package were used for visualization.RESULTS DLX1/2/3/4/5 were significantly aberrantly expressed in colon cancer patients.The expression of DLX genes were associated with M stage,pathologic stage,primary therapy outcome,residual tumor,lymphatic invasion,T stage,N stage,age,perineural invasion,and history of colon polyps.DLX5 was independently correlated with the prognosis of colon cancer in multivariate analysis.DLX1/2/3/4/5/6 were involved in the development and progression of colon cancer by participating in immune infiltration and associated pathways,including the Hippo signaling pathway,the Wnt signaling pathway,several signaling pathways regulating the pluripotency of stem cells,and Staphylococcus aureus infection.CONCLUSION The results of this study suggest a possible role for the DLX gene family as potential diagnostic or prognostic biomarkers and therapeutic targets in colon cancer.

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

Epigenetics is the study of phenotypic variations that do not alter DNA sequences.Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers.One of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer.Therefore,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences.Currently,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying loci.At each DNA methylation site,a beta value(β)is used to reflect the methylation intensity.Therefore,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user bias.This study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation sites.The efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research Institute.The performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data sets.The NBDC-GA outperformed the IGA in terms of convergence performance.Furthermore,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,respectively.The proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities that were previously linked to cancer.

Construction of a prognostic signature of m7G-related lncRNAs in bladder cancer:a bioinformatics analysis

Background:N7-methylguanosine(m7G)-related plays an important role in the occurrence and development of tumors,and some recent studies have pointed out that long non-coding RNA is involved in the occurrence and development of various cancers.However,there is no literature on how m7G-related lncRNAs predict the prognosis of bladder cancer.The purpose of this study was to develop a predictive feature based on long non-coding RNA(lncRNAs)associated with m7G to predict the prognosis of patients with bladder cancer.Methods:We obtained the RNA transcriptome data and clinical data of bladder cancer patients through the cancer genome atlas database,and obtained the lncRNAs related to m7G by co-expression analysis and Cox regression analysis.Then the signature was evaluated by receiver operating characteristic curve and nomogram,and single sample gene set concentration analysis was used to study the correlation between the predictive model and tumor immune microenvironment in high-risk and low-risk groups.Results:We got a total of 5 m7G related lncRNA(MAFG-DT,AP003352.1,AC242842.1,AC024060.1,FAM111A-DT),which may be related to the prognosis of patients with bladder cancer.For predicting 1-,3-and 5-year survival rates,the area under the receiver operating characteristic curve was 0.757 and 0.722 and 0.739,respectively.Kaplan-Meier analysis showed that the prognosis of bladder cancer patients in high risk group was worse than that in low risk group.Immunoassay showed that the immune function of patients with bladder cancer in high risk group was more active.Conclusion:Prognostic markers based on m7G-related lncRNAs can be used to independently predict the prognosis of patients with bladder cancer and provide therapeutic targets for future clinical treatment.

Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients

Objective The activities and products of carbohydrate metabolism are involved in key processes of cancer.However,its relationship with hepatocellular carcinoma(HCC)is unclear.Methods The cancer genome atlas(TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases.Differentially expressed genes(DEGs)between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes(CRGs)to obtain differentially expressed CRGs(DE-CRGs).Then,univariate Cox and least absolute shrinkage and selection operator(LASSO)analyses were applied to identify risk model genes,and HCC samples were divided into high/low-risk groups according to the median risk score.Next,gene set enrichment analysis(GSEA)was performed on the risk model genes.The sensitivity of the risk model to immunotherapy and chemotherapy was also explored.Results A total of 8 risk model genes,namely,G6PD,PFKFB4,ACAT1,ALDH2,ACYP1,OGDHL,ACADS,and TKTL1,were identified.Moreover,the risk score,cancer status,age,and pathologic T stage were strongly associated with the prognosis of HCC patients.Both the stromal score and immune score had significant negative/positive correlations with the risk score,reflecting the important role of the risk model in immunotherapy sensitivity.Furthermore,the stromal and immune scores had significant negative/positive correlations with risk scores,reflecting the important role of the risk model in immunotherapy sensitivity.Eventually,we found that high-/low-risk patients were more sensitive to 102 drugs,suggesting that the risk model exhibited sensitivity to chemotherapy drugs.The results of the experiments in HCC tissue samples validated the expression of the risk model genes.Conclusion Through bioinformatic analysis,we constructed a carbohydrate metabolism-related risk model for HCC,contributing to the prognosis prediction and treatment of HCC patients.

Prognostic value of PEA3 subfamily gene expression in cholangiocarcinoma

BACKGROUND Cholangiocarcinoma(CCA)is a lethal malignancy with limited treatment options and poor prognosis.The PEA3 subfamily of E26 transformation specific genes:ETV1,ETV4,and ETV5 are known to play significant roles in various cancers by influencing cell proliferation,invasion,and metastasis.AIM To analyze PEA3 subfamily gene expression levels in CCA and their correlation with clinical parameters to determine their prognostic value for CCA.METHODS The expression levels of PEA3 subfamily genes in pan-cancer and CCA data in the cancer genome atlas and genotype-tissue expression project databases were analyzed with R language software.Survival curve and receiver operating characteristic analyses were performed using the SurvMiner,Survival,and Procr language packages.The gene expression profiling interactive analysis 2.0 database was used to analyze the expression levels of PEA3 subfamily genes in different subtypes and stages of CCA.Web Gestalt was used to perform the gene ontology/Kyoto encyclopedia of genes and genomes(GO/KEGG)analysis,and STRING database analysis was used to determine the genes and proteins related to PEA3 subfamily genes.RESULTS ETV1,ETV4,and ETV5 expression levels were significantly increased in CCA.There were significant differences in ETV1,ETV4,and ETV5 expression levels among the different subtypes of CCA,and predictive analysis revealed that only high ETV1 and ETV4 expression levels were significantly associated with shorter overall survival in patients with CCA.GO/KEGG analysis revealed that PEA3 subfamily genes were closely related to transcriptional misregulation in cancer.In vitro and in vivo experiments revealed that PEA3 silencing inhibited the invasion and metastasis of CCA cells.CONCLUSION The expression level of ETV4 may be a predictive biomarker of survival in patients with CCA.

Data mining in clinical big data:the frequently used databases,steps,and methodological models

Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Huanglian decoction suppresses the growth of hepatocellular carcinoma cells by reducing CCNB1 expression

BACKGROUND Hepatocellular carcinoma(HCC)is one of the most prevalent cancers in human populations worldwide.Huanglian decoction is one of the most important Chinese medicine formulas,with the potential to treat cancer.AIM To investigate the role and mechanism of Huanglian decoction on HCC cells.METHODS To identify differentially expressed genes(DEGs),we downloaded gene expression profile data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma and Gene Expression Omnibus(GSE45436)databases.We obtained phytochemicals of the four herbs of Huanglian decoction from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.We also established a regulatory network of DEGs and drug target genes and subsequently analyzed key genes using bioinformatics approaches.Furthermore,we conducted in vitro experiments to explore the effect of Huanglian decoction and to verify the predictions.In particular,the CCNB1 gene was knocked down to verify the primary target of this decoction.Through the identification of the expression levels of key proteins,we determined the primary mechanism of Huanglian decoction in HCC.RESULTS Based on the results of the network pharmacological analysis,we revealed 5 bioactive compounds in Huanglian decoction that act on HCC.In addition,a protein-protein interaction network analysis of the target genes of these five compounds as well as expression and prognosis analyses were performed in tumors.CCNB1 was confirmed to be the primary gene that may be highly expressed in tumors and was significantly associated with a worse prognosis.We also noted that CCNB1 may serve as an independent prognostic indicator in HCC.Moreover,in vitro experiments demonstrated that Huanglian decoction significantly inhibited the growth,migration,and invasiveness of HCC cells and induced cell apoptosis and G2/M phase arrest.Further analysis showed that the decoction may inhibit the growth of HCC cells by downregulating the CCNB1 expression level.After Huanglian decoction treatment,the expression levels of Bax,caspase 3,caspase 9,p21 and p53 in HCC cells were increased,while the expression of CDK1 and CCNB1 was significantly decreased.The p53 signaling pathway was also found to play an important role in this process.CONCLUSION Huanglian decoction has a significant inhibitory effect on HCC cells.CCNB1 is a potential therapeutic target in HCC.Further analysis showed that Huanglian decoction can inhibit HCC cell growth by downregulating the expression of CCNB1 to activate the p53 signaling pathway.

Identification of key genes controlling cancer stem cell characteristics in gastric cancer

BACKGROUND Self-renewal of gastric cancer stem cells(GCSCs)is considered to be the underlying cause of the metastasis,drug resistance,and recurrence of gastric cancer(GC).AIM To characterize the expression of stem cell-related genes in GC.METHODS RNA sequencing results and clinical data for gastric adenoma and adenocarcinoma samples were obtained from The Cancer Genome Atlas database,and the results of the GC mRNA expression-based stemness index(mRNAsi)were analyzed.Weighted gene coexpression network analysis was then used to find modules of interest and their key genes.Survival analysis of key genes was performed using the online tool Kaplan-Meier Plotter,and the online database Oncomine was used to assess the expression of key genes in GC.RESULTS mRNAsi was significantly upregulated in GC tissues compared to normal gastric tissues(P<0.0001).A total of 16 modules were obtained from the gene coexpression network;the brown module was most positively correlated with mRNAsi.Sixteen key genes(BUB1,BUB1 B,NCAPH,KIF14,RACGAP1,RAD54 L,TPX2,KIF15,KIF18 B,CENPF,TTK,KIF4 A,SGOL2,PLK4,XRCC2,a n d C1 orf112)were identified in the brown module.The functional and pathway enrichment analyses showed that the key genes were significantly enriched in the spindle cellular component,the sister chromatid segregation biological process,the motor activity molecular function,and the cell cycle and homologous recombination pathways.Survival analysis and Oncomine analysis revealed that the prognosis of patients with GC and the expression of three genes(RAD54 L,TPX2,and XRCC2)were consistently related.CONCLUSION Sixteen key genes are primarily associated with stem cell self-renewal and cell proliferation characteristics.RAD54 L,TPX2,and XRCC2 are the most likely therapeutic targets for inhibiting the stemness characteristics of GC cells.

Identification of ITGA3 as an Oncogene in Human Tongue Cancer via Integrated Bioinformatics Analysis

Human tongue cancer （TC） is an aggressive malignancy with a very poor prognosis. There is an urgent need to elucidate the underlying molecular mechanisms involved in TC progression, mRNA expression profiles play a vital role in the exploration of cancer-related genes. Therefore, the purpose of our study was to identify the progression associated candidate genes of TC by bioinformatics analysis. Five microarray datasets of TC samples were downloaded from the Gene Expression Onmibus （GEO） database and the data of 133 TC patients were screened from The Cancer Genome Atlas （TCGA） head and neck squamous cell carcinoma （HNSC） database. The integrated analysis of five microarray datasets and the RNA sequencing data of TC samples in TCGA-HNSC was performed to obtain 1023 overlapping differentially expressed genes （DEGs） in TC and adjacent normal tissue （ANT） samples. Next, Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis was conducted to enrich the significant pathways of the 1023 DEGs and PI3K- Akt signaling pathway （P=0.011） was selected to be the candidate pathway. A total of 23 DEGs with ｜log2 fold change （FC）｜ ≥1.0 in phosphatidylinositol 3-kinase-serine/threonine kinase （PI3K-Akt） signaling pathway were subjected to survival analysis of 125 eligible TC samples in TCGA database, indicating increased integrin-α3 gene （ITGA3） expression was significantly associated with poorer prognosis. Taken together, our study suggested ITGA3 may facilitate the development of TC via activating PI3K-Akt signaling pathway.

Signature based on molecular subtypes of deoxyribonucleic acid methylation predicts overall survival in gastric cancer

BACKGROUND Gastric cancer(GC) ranks as the third leading cause of cancer-related death worldwide. Epigenetic alterations contribute to tumor heterogeneity in early stages.AIM To identify the specific deoxyribonucleic acid(DNA) methylation sites that influence the prognosis of GC patients and explore the prognostic value of a model based on subtypes of DNA methylation.METHODS Patients were randomly classified into training and test sets. Prognostic DNA methylation sites were identified by integrating DNA methylation profiles and clinical data from The Cancer Genome Atlas GC cohort. In the training set, unsupervised consensus clustering was performed to identify distinct subgroups based on methylation status. A risk score model was built based on Kaplan-Meier, least absolute shrinkage and selector operation, and multivariate Cox regression analyses. A test set was used to validate this model.RESULTS Three subgroups based on DNA methylation profiles in the training set were identified using 1061 methylation sites that were significantly associated with survival. These methylation subtypes reflected differences in T, N, and M category, age, stage, and prognosis. Forty-one methylation sites were screened as specific hyper-or hypomethylation sites for each specific subgroup. Enrichment analysis revealed that they were mainly involved in pathways related to carcinogenesis, tumor growth, and progression. Finally, two methylation sites were chosen to generate a prognostic model. The high-risk group showed a markedly poor prognosis compared to the low-risk group in both the training [hazard ratio(HR) = 2.24, 95% confidence interval(CI): 1.28-3.92, P < 0.001] and test(HR = 2.12, 95%CI: 1.19-3.78, P = 0.002) datasets.CONCLUSION DNA methylation-based classification reflects the epigenetic heterogeneity of GC and may contribute to predicting prognosis and offer novel insights for individualized treatment of patients with GC.

Identification of WWTR1 as an immune infiltration-correlated prognostic biomarker in colon cancer

WWTR1,a gene related to the TGF-βsignaling pathway,has been elucidated to be involved in oncogenesis in multiple studies.There is,however,no research on its link to immune infiltration in colon cancer.The TCGA database has identified WWTR1,a gene related to the TGF-βsignaling pathway,which is lowly expressed in colon cancer patients compared to normal subjects.Meanwhile,we produced the Kapan-Meier curve with GEO and the TCGA database,which revealed that colon cancer patients with high WWTR1 expression had a poor prognosis.We discovered that high expression of WWTR1 in colon cancer was associated with clinical stage,pathological T-stage,and lymphatic metastasis after examining the clinical characteristics of colon cancer patients.WWTR1 was found to be an independent predictive factor for colon cancer in a multivariate Cox regression study.Infiltration of immunological cells(B cells,CD8^(+)T cells,CD4^(+)T cells,Macrophage,Neutrophil,Dendritic cells)was linked to WWTR1 expression.In colon cancer,WWTR1 expression was also found to be favorably linked with major immune cell markers.According to an analysis of WWTR1 DCGs,GO,and KEGG enrichment analysis,WWTR1 expression levels were associated with ameboidal-type cell migration,focal adhesion,actin binding,Chemical carcinogenesis-reactive oxygen species,Non-alcoholic fatty liver disease,and Alzheimer disease.These findings imply that WWTR1 is a prognostically valuable and important biomarker for colon cancer,and imply that its expression is strongly linked to colon cancer immune infiltration,making it a potential new target for colon cancer biotherapy.

Eight hub genes as potential biomarkers for breast cancer diagnosis and prognosis:A TCGA-based study

BACKGROUND Breast cancer(BC)is the most common malignant tumor in women.AIM To investigate BC-associated hub genes to obtain a better understanding of BC tumorigenesis.METHODS In total,1203 BC samples were downloaded from The Cancer Genome Atlas database,which included 113 normal samples and 1090 tumor samples.The limma package of R software was used to analyze the differentially expressed genes(DEGs)in tumor tissues compared with normal tissues.The cluster Profiler package was used to perform Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis of upregulated and downregulated genes.Univariate Cox regression was conducted to explore the DEGs with statistical significance.Protein-protein interaction(PPI)network analysis was employed to investigate the hub genes using the CytoHubba plug-in of Cytoscape software.Survival analyses of the hub genes were carried out using the Kaplan-Meier method.The expression level of these hub genes was validated in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database.RESULTS A total of 1317 DEGs(fold change>2;P<0.01)were confirmed through bioinformatics analysis,which included 744 upregulated and 573 downregulated genes in BC samples.KEGG enrichment analysis indicated that the upregulated genes were mainly enriched in the cytokine-cytokine receptor interaction,cell cycle,and the p53 signaling pathway(P<0.01);and the downregulated genes were mainly enriched in the cytokine-cytokine receptor interaction,peroxisome proliferator-activated receptor signaling pathway,and AMP-activated protein kinase signaling pathway(P<0.01).CONCLUSION In view of the results of PPI analysis,which were verified by survival and expression analyses,we conclude that MAD2L1,PLK1,SAA1,CCNB1,SHCBP1,KIF4A,ANLN,and ERCC6L may act as biomarkers for the diagnosis and prognosis in BC patients.

A metabolism-associated gene signature with prognostic value in colorectal cancer

Objective In this study,our goal was to explore the role of metabolism-associated genes in colorectal cancer(CRC)and construct a prognostic model for patients with CRC.Methods Differential expression analysis was conducted using RNA-sequencing data from The Cancer Genome Atlas(TCGA)dataset.Enrichment analyses were performed to determine the function of dysregulated metabolism-associated genes.The protein-protein interaction(PPI)network,Kaplan-Meier curves,and stepwise Cox regression analyses identified key metabolism-associated genes.A prognostic model was constructed using LASSO Cox regression analysis and visualized as a nomogram.Survival analyses were conducted in the TCGA and Expression Omnibus(GEO)cohorts to demonstrate the predictive ability of the model.Results A total of 332 differentially expressed metabolism-associated genes in CRC were screened from the TCGA cohort.Differentially expressed metabolism-associated genes mainly participate in the metabolism of nucleoside phosphate,ribose phosphate,lipids,and fatty acids.A PPI network was constructed out of 328 key genes.A prognostic model was established based on five prognostic genes(ALAD,CHDH,ISYNA1,NAT1,and P4HA1)and was demonstrated to predict survival in the TCGA and GEO cohorts accurately.Conclusion The metabolism-associated prognostic model can predict the survival of patients with CRC.Our work supplements previous work focusing on determining prognostic factors of CRC and lays a foundation for further mechanistic exploration.