Research Paper Volume 14, Issue 16 pp 6689—6715
Prognostic alternative splicing events related splicing factors define the tumor microenvironment and pharmacogenomic landscape in lung adenocarcinoma
- 1 Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, P.R. China
- 2 Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, P.R. China
- 3 Department of Thoracic Surgery, The Second Hospital of Shandong University, Jinan, Shandong, P.R. China
- 4 Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University, Jinan, Shandong, P.R. China
Received: December 6, 2021 Accepted: August 9, 2022 Published: August 24, 2022
https://doi.org/10.18632/aging.204244How to Cite
Copyright: © 2022 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Recent studies identified correlations between splicing factors (SFs) and tumor progression and therapy. However, the potential roles of SFs in immune regulation and the tumor microenvironment (TME) remain unknown.
Methods: We used UpSet plots to screen for prognostic-related alternative splicing (AS) events. We evaluated SF patterns in specific immune landscapes. Single sample gene set enrichment analysis (ssGSEA) algorithms were used to quantify relative infiltration levels in immune cell subsets. Principal component analysis (PCA) algorithm-based SFscore were used to evaluate SF patterns in individual tumors with an immune response.
Results: From prognosis-related AS events, 16 prognosis-related SFs were selected to construct three SF patterns. Further TME analyses showed these patterns were highly consistent with immune-inflamed, immune-excluded, and immune-desert landscapes. Based on SFscore constructed using differentially expressed genes (DEGs) between SF patterns, patients were classified into two immune-subtypes associated with differential pharmacogenomic landscapes and cell features. A low SFscore was associated with high immune cell infiltration, high tumor mutation burden (TMB), and elevated expression of immune check points (ICPs), indicating a better immune response.
Conclusions: SFs are significantly associated with TME remodeling. Evaluating different SF patterns enhances our understanding of the TME and improves effective immunotherapy strategies.
Abbreviations
AS: Alternative splicing; NSCLC: Non-small Cell Lung Cancer; LUAD: Lung Adenocarcinoma; AA: Alternate Acceptor site; AD: Alternate Donor site; AP: Alternate Promoter; AT: Alternate Terminator; ES: Exon Skip; ME: Mutually Exclusive exons; RI: Retained Intron; TME: Tumor Microenvironment; ICI: Immune Check-point Inhibitors; DEGs: Differentially Expressed Genes; ICP: Immune Checkpoint; PCA: Principal Component Analysis; GSVA: Gene Set Variation Analysis; CNV: Copy Number Variations; TMB: Tumor Mutation Burden; IPS: Immunophenoscore; TIDE: Tumor Immune Dysfunction and Exclusion; ROC: Receiver Operating Characteristic; RFE: Recursive Feature Elimination; MAF: Mutation Annotation Format; CTLs: Cytotoxic T Lymphocytes; EMT: Epithelial-Mesenchymal Transition; SMG: Significantly Mutated Gene.