Comprehensive analysis of somatic mutator-derived and immune infiltrates related lncRNA signatures of genome instability reveals potential prognostic biomarkers involved in non-small cell lung cancer - Yang, Cai-Zhi; Yang, Ting; Liu, Xue-Ting; He, Can-Feng; Guo, Wei; Liu, Shan; Yao, Xiao-Hui; Xiao, Xi; Zeng, Wei-Ran; Lin, Li-Zhu; Huang, Zhong-Yu (Frontiers in Genetics, 2022)
Background: The function and features of long non-coding RNAs (lncRNAs) are already attracting attention and extensive research on their role as biomarkers of prediction in lung cancer. However, the signatures that are both related to genomic instability (GI) and tumor immune microenvironment (TIME) have not yet been fully explored in previous studies of non-small cell lung cancer (NSCLC). Method: The clinical characteristics, RNA expression profiles, and somatic mutation information of patients in this study came from The Cancer Genome Atlas (TCGA) database. Cox proportional hazards regression analysis was performed to construct the GIrLncSig. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the potential functions of lncRNAs. CIBERSORT was used to calculate the proportion of immune cells in NSCLC. Result: Eleven genomic instability-related lncRNAs in NSCLC were identified, then we established a prognostic model with genomic instability-related lncRNA signature (GIrLncSig) ground on the 11 lncRNAs. Through the computed GIrLncSig risk score, patients were divided into high-risk and low-risk groups. By plotting ROC curves, we found that patients in the low-risk group in the test set and TCGA set had longer overall survival than those in the high-risk group, thus validating the survival predictive power of GIrLncSig. By stratified analysis, there was still a significant difference in overall survival between high and low risk groups of patients after adjusting for other clinical characteristics, suggesting the prognostic significance of GIrLncSig is independent. In addition, combining GIrLncSig with TP53 could better predict clinical outcomes. Besides, the immune microenvironment differed significantly between the high-risk and the low-risk groups, patients with low risk scores tend to have upregulation of immune checkpoints and chemokines. Finally, we found that high-risk scores were associated with increased sensitivity to chemotherapy Conclusion: we provided a new perspective on lncRNAs related to GI and TIME and revealed the worth of them in immune infiltration and immunotherapeutic response. Besides, we found that the expression of AC027288.1 is associated with PD-1 expression, which may be a potential prognostic marker in immune checkpoint inhibitor response to improve the prediction of clinical survival in NSCLC patients.