Abstract
Background Long noncoding RNA-based prognostic biomarkers have demonstrated great potential in the diagnosis and prognosis of cancer patients. However, systematic assessment of a multiple lncRNA-composed prognostic risk model is lacking in stomach adenocarcinoma (STAD). This study is aimed at constructing a lncRNA-based prognostic risk model for STAD patients. Methods RNA sequencing data and clinical information of STAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DElncRNAs) were identified using the R software. Univariate and multivariate Cox regression analyses were performed to construct a prognostic risk model. The survival analysis, C-index, and receiver operating characteristic (ROC) curve were employed to assess the sensitivity and specificity of the model. The results were verified using the GEPIA online tool and our clinical samples. Pearson correlation coefficient analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed to indicate the potential biological functions of the selected lncRNA. Results A total of 1917 DElncRNAs were identified from 343 cases of STAD tissues and 30 cases of noncancerous tissues. According to univariate and multivariable Cox regression analyses, four DElncRNAs (AC129507.1, LINC02407, AL022316.1, and AP000695.2) were selected to establish a prognostic risk model. There was a significant difference in the overall survival between high-risk patients and low-risk patients based on this risk model. The C-index of the model was 0.652. The area under the curve (AUC) for the ROC curve was 0.769. GEPIA results confirmed the expression and prognostic significance of AP000695.2 in STAD. Our clinical data confirmed that upregulated expression of AP000695.2 was correlated with the T stage, distant metastasis, and TNM stage in STAD. GO and KEGG analyses demonstrated that AP000695.2 was closely related to the tumorigenesis process. Conclusions In this study, we constructed a lncRNA-based prognostic risk model for STAD patients. Our study will provide novel insight into the diagnosis and prognosis of STAD patients.
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