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ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 |
doi: 10.3389/fimmu.2024.1461489
This article is part of the Research Topic Application of Multi-omics Analyses in Revealing the Role of Mitochondrial Gene Defects in Disease Progression View all 10 articles
Mitochondrial-Related Genes as Prognostic and Metastatic Markers in Breast Cancer: Insights from Comprehensive Analysis and Clinical Models
Provisionally accepted- 1 Breast Center, Cancer Hospital, College of Medicine, Shantou University, Shantou, China
- 2 Jiangmen Central Hospital, Jiangmen, Guangdong, China
Background-Breast cancer (BC) constitutes a significant peril to global women's health. However, investigations delving into the correlation between mitochondrial-related genes (MRGs) and the prognosis and metastasis of BC are still infrequent.We employed the "limma" R package for differential expression analysis. Subsequently, both univariate and multivariate Cox regression analyses were executed, alongside LASSO Cox regression analysis, to pinpoint prognostic MRGs and to further develop the prognostic model. External validation (GSE88770 merged GSE425680) and internal validation were further conducted. Our investigation delved into a broad spectrum of analyses that included functional enrichment, metabolic and immune characteristics, immunotherapy response prediction, intratumor heterogeneity (ITH), mutation, tumor mutational burden (TMB), microsatellite instability (MSI), cellular stemness, single-cell, and drug sensitivity analysis. We validated the protein and mRNA expressions of prognostic MRGs in tissues and cell lines through immunohistochemistry and qRT-PCR. Moreover, leveraging the GSE102484 dataset, we conducted differential gene expression analysis to identify MRGs related to metastasis, subsequently developing metastasis models via 10 distinct machine-learning algorithms and then selecting the best-performing model. The division between training and validation cohorts was set at 70% and 30%, respectively.Results-A prognostic model was constructed by 9 prognostic MRGs.Patients within the high-risk group experienced more adverse outcomes than their counterparts in the low-risk group. The ROC curves and constructed nomogram showed that the model exhibited an excellent ability to predict overall survival (OS) for patients and the risk score was identified as an independent prognostic factor. The functional enrichment analysis showed a strong correlation between metabolic progression and MRGs. Additional research revealed that the discrepancies in outcomes between the two risk categories may be attributed to a variety of metabolic and immune characteristics, as well as differences in intratumor heterogeneity (ITH), tumor mutational burden (TMB), and cancer stemness indices.ITH TIDE and IPS analyses suggested that patients possessing a low-risk score may exhibit enhanced responsiveness to immunotherapy. Additionally, distant metastasis models were established. Among these, the XGBoost model showed the best predicting ability.In conclusion, MRGs significantly influence the prognosis and metastasis of BC.
Keywords: breast cancer, Mitochondria, Prognostic model, Metastasis model, machine-learning
Received: 08 Jul 2024; Accepted: 02 Sep 2024.
Copyright: © 2024 Yutong, Zhang, Guo, Zheng, Liu, Zhang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Cuiping Guo, Breast Center, Cancer Hospital, College of Medicine, Shantou University, Shantou, China
Bing Liu, Breast Center, Cancer Hospital, College of Medicine, Shantou University, Shantou, China
Yongqu Zhang, Breast Center, Cancer Hospital, College of Medicine, Shantou University, Shantou, China
Jundong Wu, Breast Center, Cancer Hospital, College of Medicine, Shantou University, Shantou, China
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