ArticleJournal of immunology research2022
A Novel Mitochondrial-Related Gene Signature for the Tumor Immune Microenvironment Evaluation and Prognosis Prediction in Lung Adenocarcinoma.
Article in Journal of immunology research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 13 citations in OpenAlex.
- New Functions of Mitochondrial Dysfunction in Gastric Cancer: From Molecular Processes to Potential Treatments.International journal of molecular sciences · 2026Review
- Development of a novel prognostic prediction model using mitochondrial-related genes and single-cell sequencing data for colorectal carcinoma.Translational gastroenterology and hepatology · 2025Article
- Association between strenuous sports or other exercises and lung cancer risk: a mendelian randomization study.Translational lung cancer research · 2024Article
- Impact of mitochondrial damage on tumor microenvironment and immune response: a comprehensive bibliometric analysis.Frontiers in immunology · 2024Review
- Identification of Hypoxia and Mitochondrial-related Gene Signature and Prediction of Prognostic Model in Lung Adenocarcinoma.Journal of Cancer · 2024Article
- A mitochondria-related genes associated neuroblastoma signature - based on bulk and single-cell transcriptome sequencing data analysis, and experimental validation.Frontiers in immunology · 2024Article
- Identification of mitochondrial-related signature and molecular subtype for the prognosis of osteosarcoma.Aging · 2023Article
- Role of mitochondrial alterations in human cancer progression and cancer immunity.Journal of biomedical science · 2023Review
- Construction of an endoplasmic reticulum stress-related signature in lung adenocarcinoma by comprehensive bioinformatics analysis.BMC pulmonary medicine · 2023Article
- BTG2 and SerpinB5, a novel gene pair to evaluate the prognosis of lung adenocarcinoma.Frontiers in immunology · 2023Article
- A novel signature model based on mitochondrial-related genes for predicting survival of colon adenocarcinoma.BMC medical informatics and decision making · 2022Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung adenocarcinoma (LUAD) remains the most common deadly disease and has a poor prognosis. More and more studies have reported that mitochondrial-related genes (MTRGs) were associated with the clinical outcomes of multiple tumors solely. In this study, we aimed to develop a novel prognostic model based on MTRGs. Differentially expressed MTRGs were identified from TCGA-LUAD and GSE31210 cohorts. Univariate Cox regression analysis was utilized to screen differentially expressed MTRGs that were related to prognosis of LUAD. Then, LASSO Cox regression analysis was used to develop a prognostic signature. ESTIMATE was used for estimating the fractions of immune cell types. In this study, we identified 44 overlapping differentially expressed MTRGs in TCGA-LUAD and GSE31210 cohorts. Among 44 overlapping differentially expressed MTRGs, nine genes were associated with prognosis of LUAD. When the penalty parameter lambda was the minimum, there were six genes meeting the conditions of constructing the signature, including SERPINB5, CCNB1, FGR MAOB, SH3BP5, and CYP24A1. The survival analysis suggested that prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. Cox regression analyses showed that the risk score was an independent predictor of LUAD prognosis. As with the results of ESTIMATE score, the degree of immune cell infiltration in the low-risk group was higher than that in the high-risk group, such as TIL, Treg, and B cells. In addition, TMB and cancer stem cell infiltration were higher in the low-risk group than the high-risk group. In conclusion, we developed a novel MTRG signature acting as a negative independent prognostic factor. In the future, individualized treatments and medical decision-making may benefit from using the predicted model.
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