Introduction of LPIN1 as a potential diagnostic and prognostic biomarker for gastric cancer via integrative bioinformatics analysis of a competing endogenous RNA network and experimental validation.

Abstract

Objectives: Identification of effective biomarkers is crucial for the heterogeneous disease of gastric cancer (GC). Recent studies have focused on the role of pseudogenes regulating gene expression through competing endogenous RNA (ceRNA) networks, however, the pseudogene-associated ceRNA networks in GC remain largely unknown. The current study aimed to construct and analyze a three-component ceRNA network in GC and experimentally validate a ceRNA.

Materials and methods: A comprehensive analysis was conducted on the RNA-seq and miRNA-seq data of The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) dataset to identify differentially-expressed mRNAs (DEMs), pseudogenes (DEPs), and miRNAs (DEMis). Pseudogene-associated ceRNA and protein-protein interaction (PPI) networks were constructed, and functional enrichment analyses were performed. DEMs and DEPs with degree centralities≥2 were selected for survival analysis. A ceRNA was further selected for experimental validation.

Results: 10,145 DEMs, 3576 DEPs, and 66 DEMis were retrieved and a ceRNA network was then constructed by including DEMis with concurrent interactions with at least a DEM and a DEP. Functional enrichment analysis demonstrated that DEMs of the ceRNA network were significantly enriched in cancer-associated pathways. LPIN1 and WBP1L were two mRNAs showing an association with STAD patients overall survival. Expression analysis of LPIN1 showed a significant decrease in GC tumors compared to non-tumor tissues (P=0.003).

Conclusion: Our research emphasizes the significant implications of ceRNA networks in the development of new biomarkers for the detection and prognosis of cancer. Further examination is necessary to explore the functional roles of LPIN1 in the pathogenesis of GC.