RNA modification writer-based immunological profile and genomic landscape of tumor microenvironment in lung adenocarcinoma.

Background: Recent studies have highlighted the role of RNA modification, that is, the dysregulation of epitranscriptomics, in tumorigenesis and progression. The potential for undoing epigenetic changes may develop novel therapeutic and prognostic approaches. However, the roles of these RNA modifications in the tumor microenvironment (TME) are still unknown.

Methods: We assessed the expression properties and genetic alterations of 26 RNA modification writers, including adenosine-to-inosine RNA editing, alternative polyadenylation, m1A, and m6A in 502 lung adenocarcinoma (LUAD) samples from the Cancer Genome Atlas (TCGA) datasets. Then, we used differentially expressed gene (DEGs) to develop a signature for predicting patient outcomes, which was dubbed the "writer score" for RNA-modified writers. In addition, we analyzed the association between TME features, molecular subtypes, treatment sensitivity, and immunotherapy efficacy.

Results: We comprehensively evaluated the changes in multilayer RNA modification writers and identified the role of RNA modification writer expression imbalances in LUAD emergence and progression. Additionally, we constructed a risk-score model based on six LUAD prognosis-associated differentially expressed RNA modification writer genes. Kaplan-Meier (K-M) analyses revealed that the low risk-score signature had high overall patient survival. The predictive significance of the risk-score model was demonstrated using both univariate and multivariate Cox analyses. The risk-score model was positively correlated with the immune- and proliferation-related pathways. In response to anti-cancer treatment, high-risk score is related with high TMB, which has been discovered to correlate with immunotherapy effectiveness.

Conclusion: This study showed a strong correlation between the TME variety, level of complexity, and the four types of RNA modification writers. In addition, this scoring system could potentially predict effective immunotherapy and deepens our understanding of TME characteristics.