Dataset Information

Identification and validation of the mitochondrial function related hub genes by unsupervised machine learning and multi-omics analyses in lung adenocarcinoma.

ABSTRACT:

Background

The mitochondrion and its associated genes were heavily implicated in developing and therapy tumors as the primary cellular organelle in charge of metabolic reprogramming and ferroptosis. Our work focuses on discovering new potential targets while analyzing the multi-omics data of mitochondria-related genes in lung adenocarcinoma (LUAD).

Methods

The Cancer Genome Atlas (TCGA) database provided multi-omics data for LUAD patients. Based on the expression profile of the genes associated with mitochondria, the patients were grouped by the unsupervised clustering method. R was used to explore the differential expressed protein-code gene, miRNA, and lncRNA, as well as their enriched functions and ceRNA networks. Additionally, the discrepancy between immune infiltration and genetic variation was comprehensively characterized. Our clinical samples and in vitro experiments investigated the hub gene determined by LASSO and batch analysis.

Results

Two clusters are distinguished using unsupervised consensus clustering based on mitochondrial heterogeneity. The integrated analysis emphasized that patients in cluster B had a worse prognosis, higher mutation frequencies, and less immune cell infiltration. The hub genes DARS2 and COX5B are identified by further analysis using LASSO penalization. In vitro experiments indicated that DARS2 and COX5B knockdown inhibited tumor cell proliferation. The specimen of our hospital cohort conducted the immunohistochemistry analysis and validated that DARS2 and COX5B's expression was significantly higher in the tumor than in adjacent normal tissue and correlated to LUAD patients' prognosis.

Conclusion

Our observations implied that LUAD patients' tumors had distinct mitochondrial function heterogeneity with different clinical and molecular characteristics. DARS2 and COX5B might be critical genes involved in mitochondrial alterations and potential therapeutic targets.

SUBMITTER: Jin X

PROVIDER: S-EPMC9732315 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Identification and validation of the mitochondrial function related hub genes by unsupervised machine learning and multi-omics analyses in lung adenocarcinoma.

Jin Xing X Zhang Huan H Sui Qihai Q Li Ming M Liang Jiaqi J Hu Zhengyang Z Cheng Ye Y Zheng Yuansheng Y Chen Zhencong Z Lin Miao M Wang Hao H Zhan Cheng C

Heliyon 20221201 12

<h4>Background</h4>The mitochondrion and its associated genes were heavily implicated in developing and therapy tumors as the primary cellular organelle in charge of metabolic reprogramming and ferroptosis. Our work focuses on discovering new potential targets while analyzing the multi-omics data of mitochondria-related genes in lung adenocarcinoma (LUAD).<h4>Methods</h4>The Cancer Genome Atlas (TCGA) database provided multi-omics data for LUAD patients. Based on the expression profile of the ge ...[more]

PMID: 36506395

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Project description:PurposeOsteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA development.Materials and methodsWe acquired seven bulk RNA-seq datasets from the Gene Expression Omnibus (GEO) database and examined the expression levels of differentially expressed genes related to mitochondria in OA. We utilized single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) analyses to explore the functional mechanisms associated with these genes. Seven machine learning algorithms were utilized to identify hub mitochondria-related genes and develop a predictive model. Further analyses included pathway enrichment, immune infiltration, gene-disease relationships, and mRNA-miRNA network construction based on these hub mitochondria-related genes. genome-wide association studies (GWAS) analysis was performed using the Gene Atlas database. GSEA, gene set variation analysis (GSVA), protein pathway analysis, and WGCNA were employed to investigate relevant pathways in subtypes. The Harmonizome database was employed to analyze the expression of hub mitochondria-related genes across various human tissues. Single-cell data analysis was conducted to examine patterns of gene expression distribution and pseudo-temporal changes. Additionally, The real-time polymerase chain reaction (RT-PCR) was used to validate the expression of these hub mitochondria-related genes.ResultsIn OA, the mitochondria-related pathway was significantly activated. Nine hub mitochondria-related genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4) were identified. They constructed predictive models with good ability to predict OA. These genes are primarily associated with macrophages. Unsupervised consensus clustering identified two mitochondria-associated isoforms that are primarily associated with metabolism. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR showed that they were all significantly expressed in OA.ConclusionSIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4 are potential mitochondrial target genes for studying OA. The classification of mitochondria-associated isoforms could help to personalize treatment for OA patients.

Project description:Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

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Dataset Information

Identification and validation of the mitochondrial function related hub genes by unsupervised machine learning and multi-omics analyses in lung adenocarcinoma.

Background

Methods

Results

Conclusion

Publications

Identification and validation of the mitochondrial function related hub genes by unsupervised machine learning and multi-omics analyses in lung adenocarcinoma.

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