Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

Abstract

Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored.Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset.Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis.Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.

Prognostic markers of ferroptosis-related long non-coding RNA in lung adenocarcinomas

“…However, no study to date had demonstrated the relationship between APTR and lung cancer. It showed that AC008278.2 was a protective lncRNA was one of 19 genomic instability-related lncRNAs that correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients (Zhang et al, 2022). While as for PAN3-AS1 and AC093911.1, little has been studied in current diseases or molecular mechanisms.…”

Integrative analysis reveals an enhancer based prognostic risk model for non-small cell lung cancer

“…Another study evaluated the prognostic risk of NSCLC patients using the expression level of 19 lncRNAs: LINC01067* (-0.1595) + AC012645. [33]. Another investigation predicted survival of patients with ADC directly using the methylation level of three sites:…”

RAB42 overexpression correlates with poor prognosis, immune cell infiltration and chemoresistance

“…The prognostic value of RAB42 for cancer patients was assessed by 4 clinical outcomes: overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS) and disease-free survival (DFS). Prognostic indicators, including hazard ratios (HRs), 95% confidence intervals (95% CIs) and p values, were calculated under standard conditions ( Zhang et al, 2022 ; Su et al, 2024 ). A result was considered significant when p < 0.05.…”

“…R packages “pROC” and “ggplot2” were used to determine the receiver operating characteristic (ROC)-area under the curve (AUC) values of RAB42. In principle, AUC values greater than 0.8 are considered to be high reliability ( Zhang et al, 2022 ).…”