Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39380996", "kind": "science-record", "revision": 5, "content_hash": "sha256:8bb843e9733a1ea4b6b84c33dcfd99ea7dc400403298d9ea0ec4c21bd8ed2f46", "created_at": "2026-11-01T20:29:36Z", "updated_at": "2026-11-01T23:54:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.", "doi": "10.3389/fimmu.2024.1461489", "pmid": "39380996", "authors": ["Yutong Fang", "Qunchen Zhang", "Cuiping Guo", "Rongji Zheng", "Bing Liu", "Yongqu Zhang", "Jundong Wu"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Breast cancer (BC) constitutes a significant peril to global women's health. Contemporary research progressively suggests that mitochondrial dysfunction plays a pivotal role in both the inception and advancement of BC. However, investigations delving into the correlation between mitochondrial-related genes (MRGs) and the prognosis and metastasis of BC are still infrequent.\nMethods: Utilizing data from the TCGA database, 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.\nResults: A prognostic model was constructed by 9 prognostic MRGs, which were DCTPP1, FEZ1, KMO, NME3, CCR7, ISOC2, STAR, COMTD1, and ESR2. 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 by PDK4, NRF1, DCAF8, CHPT1, MARS2 and NAMPT. Among these, the XGBoost model showed the best predicting ability.\nConclusion: In conclusion, MRGs significantly influence the prognosis and metastasis of BC. The development of dual clinical prediction models offers crucial insights for tailored and precise therapeutic strategies, and paves the way for exploring new avenues in understanding the pathogenesis of BC.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox regression analyses were performed to identify potential MRGs associated with OS. By constructing gene expression and survival object matrices, the Lasso-Cox regression model was fitted using the “glmnet” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39380996", "locator": "sections[4] \"Construction and validation of prognostic model based on MRGs\" paragraph 2", "quote": "Univariate and multivariate Cox regression analyses were performed to identify potential MRGs associated with OS. By constructing gene expression and survival object matrices, the Lasso-Cox regression model was fitted using the “glmnet” package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39380996", "locator": "sections[34] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The XGBoost model achieved an AUC of 0.951 in the training set and the highest validation-set AUC of 0.778, making it the optimal model for predicting distant metastasis in breast cancer patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39380996", "locator": "sections[28] \"Establishment and evaluation of metastatic model\" paragraph 2", "quote": "Compared with the other models, the XGBoost model not only demonstrated an excellent AUC of 0.951 in the training set but also achieved the highest AUC of 0.778 in the validation set ( Figure 9A )."}, {"asset": "source:pmid:39380996", "locator": "sections[28] \"Establishment and evaluation of metastatic model\" paragraph 2", "quote": "Therefore, in terms of comprehensive model evaluation, XGBoost was the optimal model for predicting metastasis in BC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The XGBoost model achieved an AUC of 0.951 in the training set and the highest validation-set AUC of 0.778, making it the optimal model for predicting distant metastasis in breast cancer patients.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39380996"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE102484"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE102484"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39380996", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2024.1461489", "sha256": 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