Genome-scale analysis to identify prognostic markers and predict the survival of lung adenocarcinoma.

Lung cancer is one of the most malignant cancers worldwide, and lung adenocarcinoma (LAC) remains the most common histologic subtype. However, the functional significance of RNA expression-based prognosis prediction in LAC is still unclear and needs to be further studied. By utilizing the Cox multivariate regression, we established a risk score staging system to predict the outcome of patients with LAC and subsequently identified 10 genes, including PTPRH, OGFRP1, LDHA, AL365203.1, LINC02178, AL512488.1, LINC01312, AL353746.1, DRAXINP1, and LINC02310, which were closely related to the prognosis of patients with LAC. The identified genes allowed us to classify patients into high-risk group with poor outcome and low-risk group with better outcome. Compared with other clinical factors, the risk score performs better in predicting the outcome of LAC patients. We used Gene-Set Enrichment Analysis to identify the differences between the 2 groups in biological pathways. In conclusion, we identified 10 genes by utilizing Cox regression model and developed a risk staging model for LAC, which might prove significant for the clinical management of LAC patients.