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.
Background: Neuron specific enolase (NSE) is a specific biomarker for SCLC. However, the biological roles and aberrant expression of NSE in SCLC have not been well illustrated. Methods: The expression of NSE, miR-93-5p and LINC00657 in SCLC tissues and cell lines were detected using real time quantitative PCR (qRT-PCR) or immunohistochemistry. CCK8 assay was performed to detect cell proliferation. Cell migration and invasion capabilities were investigated by transwell assay. Epithelial-mesenchymal transition (EMT) process was verified by detecting epithelial marker E-cadherin and mesenchymal marker N-cadherin. The direct interactions between miR-93-5p and NSE or LINC00657 were predicted by bioinformatics tools and verified using dual luciferase reporter assay. Results: Upregulated expression of NSE in SCLC tumor tissues were positively associated with advanced tumor stage, distant metastasis and poor overall survival. Overexpression of NSE promoted cell proliferation, migration, invasion and EMT in SCLC cells, while silence of NSE inhibited these effects. Mechanically, NSE expression was positively correlated with LINC00657, and negatively correlated with miR-93-5p. Moreover, NSE was positively regulated by LINC00657 through sponging of miR-93-5p. LINC00657 and miR-93-5p promoted SCLC cell migration, invasion and EMT by NSE-mediated manner. Conclusion: Overall, our study revealed a novel role of NSE in SCLC. NSE was positively regulated by LINC00657 through competitively interacting with miR-93-5p, which may be potential targets for SCLC patients.
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