Identification of anoikis-related long non-coding RNA signature as a novel prognostic model in lung adenocarcinoma.

BACKGROUND

Anoikis, as a specific form of programmed cell death, involves in tumor metastasis. However, there is still lacking of anoikis-related long non-coding RNA (lncRNA) risk signature in the diagnosis and prognosis of lung adenocarcinoma (LUAD). This study constructed a prognostic risk model by comprehensively analyzing anoikis-related lncRNAs which could effectively diagnose and predict the outcomes of LUAD patients.

METHODS

A list of anoikis-related genes (ARGs) was retrieved from literatures. Anoikis-related lncRNAs were selected using co-expression analysis from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate regression analyses were used to construct a prognostic model. The performance of the risk signature in predicting the prognosis and clinical significance were determined by Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, univariate and multivariate regression analyses. Moreover, the differences of tumor immune microenvironment between the high- and low-risk groups were explored. Finally, a novel nomogram was developed by combining the signature and clinicopathological factors, and the association between lncRNAs and differential N6-methyladenosine (m6A) genes was analyzed by Spearman's analysis.

RESULTS

A total of 1,694 anoikis-related lncRNAs were identified from 479 cases of LUAD. According to the univariate and multivariate Cox analyses, we established a prognostic risk model consisting of seven lncRNAs (AC026355.2, AL606489.1, AL031667.3, LINC02802, LINC01116, AC018529.1, and AP000844.2). This prognostic risk model could efficiently classify low- and high-risk patients. The area under the curve (AUC) value was 0.717, which indicated more powerful predictive capability than commonly used clinicopathological factors. The high- and low-risk groups demonstrated different immune microenvironment. Moreover, the nomogram also demonstrated good performance in predicting the prognosis. Twelve differential m6A regulators were identified, and RBM15 was found to be correlated positively with the hub lncRNA AL606489.1.

CONCLUSIONS

Our study constructed a prognostic risk model based on anoikis-related lncRNAs, which could provide novel perspective on the prognosis of LUAD patients.