Research Paper Volume 16, Issue 10 pp 8772—8809
Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses
- 1 Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan 250021, Shandong, China
- 2 Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
- 3 Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
- 4 Institute of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
Received: October 24, 2023 Accepted: April 16, 2024 Published: May 20, 2024
https://doi.org/10.18632/aging.205838How to Cite
Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.
Abbreviations
LUAD: Lung adenocarcinoma; TME: Tumor microenvironment; NK: Natural killer; FA: Fatty acid; MPPS: Metabolic pathway-pair score; FPKM: Fragments per kilobase million; KEGG: Kyoto Encyclopedia of Genes and Genomes; TCGA: The Cancer Genome Atlas; GEO: Gene-Expression Omnibus; CNV: Copy number variation; NSCLC: Non-small cell lung cancer; TIDE: Tumor immune dysfunction and exclusion; INFG: Interferon gamma; MDSCs: Myeloid-derived suppressor cells; MSI: Microsatellite instability; CAF: Cancer associated fibroblast; TAM: Tumor-associated macrophages; ssGSEA: Single sample gene set enrichment analysis; PCA: Principal Component Analysis; scRNA-seq: Single-cell RNA sequencing; LASSO: Least absolute shrinkage and selection operator; GO: Gene Ontology; PPI: Protein-protein interaction; DEGs: Differentially expressed genes; TMB: Tumor mutation burden; WGCNA: Weighted gene co-expression network analysis; GS: Gene significance; MM: Module membership; TOM: Topological overlap matrix; RSF: Random survival forest; Enet: Elastic network; plsRcox: Partial least squares regression for Cox; GBM: Generalised boosted regression modelling; SuperPC: Supervised principal components; survival-SVM: Survival support vector machine; LOOCV: Leave-one-out cross-validation; CM: Conditional medium; PMA: Phorbol 12-myristate 13-acetate; FBS: Fetal bovine serum; EMT: Epithelial-mesenchymal transition; IPS: Immunophenoscore; ACT: Adoptive T cell therapy; BCAAs: Branched-chain amino acids; GWAS: Genome wide association study; IVW: Inverse variance weighting.