Enhanced multi-omics analysis reveals a lncRNA signature with 12 RNA modifications to predict tumor heterogeneity and potential therapy in non-small cell lung cancer

Abstract

This study delves into the landscape of RNA modification (RM)-related long non-coding RNAs (lncRNAs) within non-small cell lung cancer (NSCLC). We aim to uncover their significance in cancer biology and potential clinical implications. We utilized diverse datasets to identify 444 RM-related genes with 12 RMs. RM scores were computed, and associations with survival were analyzed. Weighted gene co-expression network analysis identified 730 RM-related lncRNAs. Univariate Cox regression identified 63 prognostically significant lncRNAs, leading to the classification of NSCLC samples into two clusters. Distinct differences in overall survival and disease-free interval were observed between the identified lncRNA clusters, showcasing their prognostic relevance. Molecular characterization uncovered mutation landscape variations, with cluster 2 displaying higher mutation rates in TP53 and TTN. Cluster-specific genomic alterations, immune cell infiltration, and immune checkpoint gene expression patterns were identified. Drug sensitivity analysis revealed distinct profiles, with cluster 1 showing potential resistance to a combined approach of certain chemotherapy and immunotherapy, while cluster 2 may be suitable for monotherapy with specific chemotherapeutic or targeted agents. In conclusion, this study stands as the first and most comprehensive exploration, elucidating the intricate connections between RM, lncRNAs, NSCLC, and tumor immunity. Its findings significantly enhance our comprehension of NSCLC heterogeneity, offering pivotal insights and paving the path toward personalized treatment strategies.