LASSO‑based Cox‑PH model identifies an 11‑lncRNA signature for prognosis prediction in gastric cancer

Abstract

The present study aimed to identify a long non-coding (lnc) RNAs-based signature for prognosis assessment in gastric cancer (GC) patients. By integrating gene expression data of GC and normal samples from the National Center for Biotechnology Information Gene Expression Omnibus, the EBI ArrayExpress and The Cancer Genome Atlas (TCGA) repositories, the common RNAs in Genomic Spatial Event (GSE) 65801, GSE29998, E-MTAB-1338, and TCGA set were screened and used to construct a weighted correlation network analysis (WGCNA) network for mining GC-related modules. Consensus differentially expressed RNAs (DERs) between GC and normal samples in the four datasets were screened using the MetaDE method. From the overlapped lncRNAs shared by preserved WGCNA modules and the consensus DERs, an lncRNAs signature was obtained using L1-penalized (lasso) Cox-proportional hazard (PH) model. LncRNA-mRNA networks were constructed for these signature lncRNAs, followed by functional annotation. A total of 14,824 common mRNAs and 2,869 common lncRNAs were identified in the 4 sets and 5 GC-associated WGCNA modules were preserved across all sets. MetaDE method identified 1,121 consensus DERs. A total of 50 lncRNAs were shared by preserved WGCNA modules and the consensus DERs. Subsequently, an 11-lncRNA signature was identified by LASSO-based Cox-PH model. The lncRNAs signature-based risk score could divide patients into 2 risk groups with significantly different overall survival and recurrence-free survival times. The predictive capability of this signature was verified in an independent set. These signature lncRNAs were implicated in several biological processes and pathways associated with the immune response, the inflammatory response and cell cycle control. The present study identified an 11-lncRNA signature that could predict the survival rate for GC.

Identification of Long Noncoding RNA Associated ceRNA Networks in Rosacea

“…Dysregulation of lncRNAs has been reported to be associated with several human diseases, including autoimmune diseases and different types of tumors [7,8,21,[36][37][38][39]. Recently, lncRNAs broad expression in cutaneous disorders including systemic lupus erythematosus (SLE), sensitive skin, psoriasis, and aging process has been documented [40][41][42][43].…”

Comprehensive analysis of immune prognostic‐related genes in the tumor microenvironment of hepatocellular carcinoma

“…Successful validation of the prognostic performance of the risk scoring model was conducted in an independent set from GEO. It shows that the seven-genes ImmuneRiskScore are promising prognostic biomarkers for HCC and may function importantly in the TME of HCC.The pipeline constituted by WGCNA and lasso cox is effective and valided by many cancer types, such as glioblastoma [56], prostate [57], gastric cancer [58] , lung cancer [59] and bladder cancer [60]. So far, there has only two study applying WGCNA and lasso cox regression analysis to study the HCC but no research for the immune microenvironment.…”

Immune-related Gene Data-based Molecular Subtyping Related to the Prognosis for Breast Cancer Patients

“…There was no signi cant difference between training set and test set data, as veri ed by the Pvalue, which had indicated reasonable sample grouping. Single-factor survival analysis of immune-related genes in the training set All immune-related genes were analyzed using the univariate Cox proportional hazards regression model; at the same time, survival data were evaluated by the survival coxph function of R package [24], and p < 0.05 served as the signi cance threshold.…”