https://orcid.org/0000-0002-9563-983X
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ABSTRACT
Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.
Abbreviations
| ccRCC | = | clear cell renal cell carcinoma |
| MOICS | = | multiomics immune-based cancer subtype |
| RCC | = | Renal cancer cell |
| TIME | = | tumor immunological microenvironment |
| ICIs | = | immune checkpoint inhibitors |
| TMB | = | tumor mutation burden |
| MSI | = | microsatellite instability |
| CNV | = | copy number variation |
| CPI | = | Clustering Prediction Index |
| DEG | = | differentially expressed gene |
| ssGSVA | = | single-sample gene set enrichment analysis |
| GO | = | Gene Ontology |
| KEGG | = | Kyoto Encyclopedia of Genes and Genomes |
| GSEA | = | Gene Set Enrichment Analysis |
| GSVA | = | Gene Set Variation Analysis |
| TIDE | = | Tumor Immune Dysfunction and Exclusion |
| CNA | = | copy number alterations |
| GDSC | = | Genomics of Cancer Drug Sensitivity |
| IC50 | = | half-maximal inhibitory concentration |
| RSFVH | = | Random Survival Forest Variable Hunting |
| HR | = | hazard ratio |
| MeTIL | = | methylation of tumor-infiltrating lymphocytes |
| RNAss | = | RNA expression-based stemness index |
| ENHSS | = | enhancer element stemness score |
| HRD | = | homologous recombination deficiency |
| BP | = | biological process |
| CC | = | cellular compartment |
| MF | = | molecular function |
| NTP | = | nearest template prediction |
| OS | = | Overall survival |
| PFS | = | Progression-free survival |
| TBR | = | TGFβ response |
| HLA | = | human leukocyte antigens |
| EPAS1 | = | endothelial PAS domain protein 1 |
| HIF-2α | = | hypoxia-inducible factor 2-alpha. |
Acknowledgments
We thank Dr. Jianming Zeng (University of Macau), and all the members of his bioinformatics team, Biotrainee, for generously sharing their experience and codes. The use of the biorstudio high performance computing cluster (https://biorstudio.cloud) at Biotrainee and the shanghai HS Biotech Co., Ltd. for conducting the research reported in this paper.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Author contributions
Ying Liu, Lin Qi and Bicheng Ye performed formal analysis and contributed equally to this work. Aimin Jiang, Linhui Wang, Peng Luo and Le Qu designed this study. Anbang Wang, Juan Lu and Le Qu wrote the first draft. All listed authors have read and approved the final submitted version.
Consent for publication
All authors contributed to the article and approved the submitted version.
Data availability statement
Access details of the public datasets used in this work can be found in the methods and materials section of the study.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/15384047.2024.2345977