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

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.