Identification and Validation of a Potent Multi-lncRNA Molecular Model for Predicting Gastric Cancer Prognosis

Gastric cancer (GC) remains the third deadliest malignancy in China. Despite the current understanding that the long noncoding RNAs (lncRNAs) play a pivotal function in the growth and progression of cancer, their prognostic value in GC remains unclear. Therefore, we aimed to construct a polymolecular prediction model by employing a competing endogenous RNA (ceRNA) network signature obtained by integrated bioinformatics analysis to evaluate patient prognosis in GC. Overall, 1,464 mRNAs, 14,376 lncRNAs, and 73 microRNAs (miRNAs) were found to be differentially expressed in GC. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that these differentially expressed RNAs were mostly enriched in neuroactive ligand-receptor interaction, chemical carcinogenesis, epidermis development, and digestion, which were correlated with GC. A ceRNA network consisting of four lncRNAs, 21 miRNAs, and 12 mRNAs were constructed. We identified four lncRNAs (lnc00473, H19, AC079160.1, and AC093866.1) as prognostic biomarkers, and their levels were quantified by qRT-PCR in cancer and adjacent noncancerous tissue specimens. Univariable and multivariable Cox regression analyses suggested statistically significant differences in age, stage, radiotherapy, and risk score groups, which were independent predictors of prognosis. A risk prediction model was created to test whether lncRNAs could be used as an independent risk predictor of GC or not. These novel lncRNAs' signature independently predicted overall survival in GC (p < 0.001). Taken together, this study identified a ceRNA and protein-protein interaction networks that significantly affect GC, which could be valuable for GC diagnosis and therapy.