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Construction and multi-omics analysis of ccRCC mitochondrial related gene machine learning model and validate of key
Shihang Pu1, Shijie Qian1, Jiajun Xing1
1Department of Urology, First Affiliated Hospital of Nanjing Medical University, No.300, Guangzhou Street, Nanjing, Jiangsu Province 210029, China.
Background:
Clear cell renal cell carcinoma represents the most prevalent histological subtype of renal malignancy Emerging evidence underscores the critical involvement of mitochondrial dysfunction in oncogenesis and tumor progression. In this study, we systematically identified and characterized mitochondrial-associated proteins participating in fundamental biological processes, with the objective of elucidating their mechanistic contributions to ccRCC pathogenesis.
Method:
Through integrative analysis of multi-omics data from TCGA-KIRC and GEO datasets, we systematically evaluated the prognostic value of mitochondrial-associated differentially expressed genes derived from the MitoCarta3.0 database. Employing an ensemble machine learning approach encompassing 10 base algorithms and their combinatorial variants, we developed and optimized a robust prognostic risk model. Subsequent experimental validation focused on the core model gene FKBP10, investigating its functional role in ccRCC pathogenesis through comprehensive in vivo analyses.
Result:
We identified 102 mitochondrial-related differentially expressed genes showing significant tumor-normal tissue expression differences and strong prognostic associations. The LASSO+GBM algorithm was selected as the optimal model, from which we derived 7 core prognostic genes. Comprehensive characterization of high- and low-risk groups revealed distinct patterns in pathway activation, mutational profiles, immune microenvironment composition, and therapeutic vulnerabilities. Experimental validation confirmed that the key gene FKBP10 promotes malignant phenotypes in tumor cells through IL-6/JAK/STAT3 pathway activation.
Insights
Mitochondrial dysfunction is key in clear cell renal cell carcinoma (ccRCC). We identified 7 core genes, including FKBP10, which drives ccRCC via the IL-6/JAK/STAT3 pathway, offering new therapeutic targets.
Area of Science:
- Oncology
- Mitochondrial Biology
- Bioinformatics
Background:
- Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer.
- Mitochondrial dysfunction is increasingly recognized as a driver of cancer development and progression.
- Understanding mitochondrial roles in ccRCC is crucial for identifying new therapeutic strategies.
Purpose of the Study:
- To identify and characterize mitochondrial-associated proteins involved in ccRCC pathogenesis.
- To develop a prognostic risk model for ccRCC based on mitochondrial genes.
- To experimentally validate the role of a key identified gene in ccRCC.
Main Methods:
- Integrative analysis of multi-omics data (TCGA-KIRC, GEO) and MitoCarta3.0 database.
- Development and optimization of a prognostic risk model using ensemble machine learning (10 algorithms).
- In vivo experimental validation of the core gene FKBP10 in ccRCC.
Main Results:
- Identified 102 mitochondrial-related differentially expressed genes with prognostic significance.
- Selected LASSO+GBM algorithm to derive a 7-core-gene prognostic model.
- FKBP10 promotes ccRCC malignancy via IL-6/JAK/STAT3 pathway activation, with distinct high-risk and low-risk group characteristics.
Conclusions:
- The study provides a robust prognostic model for ccRCC based on mitochondrial genes.
- FKBP10 is a key driver of ccRCC progression through the IL-6/JAK/STAT3 pathway.
- These findings highlight potential therapeutic targets for ccRCC treatment.
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