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.

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).

Prognostic risk model construction and prognostic biomarkers identification in esophageal adenocarcinoma based on immune-related long noncoding RNA

Objective The aim of this study was to construct a prognostic model of esophageal adenocarcinoma(EAC)based on immune-related long noncoding RNAs(immune-related lncRNAs)and identify prognostic biomarkers using the Cancer Genome Atlas(TCGA)database.Methods Whole genomic mRNA expression and clinical data of esophageal adenocarcinoma were obtained from the TCGA database.The software Strawberry Perl,R and R packets were used to identify the immune-related genes and lncRNAs of esophageal adenocarcinoma,and for data processing and analysis.The differentially expressed lncRNAs were detected while comparing esophageal adenocarcinoma and normal tissue samples.The key immune-related lncRNAs were screened using lasso regression analysis and univariate cox regression analysis,and used to construct the prognostic model using multivariate cox regression analysis.To evaluate the accuracy of the risk prognostic model,all esophageal adenocarcinomas were divided into high-risk and low-risk groups according to the median risk score,after which Kaplan-Meier(K-M)survival curves,operating characteristic(ROC)curve and independent prognostic analysis of clinical traits were created.In addition,statistically significant immune-related lncRNAs and potential prognostic biomarkers were identified using the prognostic model and multifactor cox regression analysis for k-m survival analysis.Results A total of 1322 differentially expressed immune-related lncRNAs were identified,28 of which were associated with prognosis via univariate cox regression analysis.In addition,K-M survival analysis showed that the total survival time of the higher risk group was significantly shorter than that of the lower risk group(P=1.063e-10).The area under the ROC curve of 5-year total survival rate was 0.90.The risk score showed independent prognostic risk for esophageal adenocarcinoma via single factor and multifactorial independent prognostic analyses.In addition,the HR and 95%CI of each key immune-related lncRNA were calculated using multivariate Cox regression.Using k-m survival analysis,we found that 5 out of 12 key significant immune-related lncRNAs had independent prognostic value[AL136115.1(P=0.006),AC079684.1(P=0.008),AC07916394.1(P=0.0386),AC087620.1(P=0.041)and MIRLET7BHG(P=0.044)].Conclusion The present study successfully constructed a prognostic model of esophageal adenocarcinoma based on the TCGA database,with moderate predictive accuracy.The model consisted of the expression level of 12 immune-related lncRNAs.Furthermore,the study identified one favorable prognostic biomarker,MIRLET7BHG,and four poor prognostic biomarkers(AL136115.1,AC079684.1,AC016394.1,and AC087620.1).