ArticleJournal of translational medicine2025
Identification and validation of a prognostic signature of drug resistance and mitochondrial energy metabolism-related differentially expressed genes for breast cancer.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- LRP2-mediated regulation of ferroptosis through the Wnt/β-catenin-GPX4 axis in colorectal cancer liver metastasis and chemoresistance.Cell death discovery · 2026Article
- HDHD5 promotes triple-negative breast cancer growth and drives EMT-associated phenotypes via regulating S100A4.Journal of cancer research and clinical oncology · 2026Article
- Identification and validation of a novel estrogen-related model for breast cancer to predict the prognosis.Translational cancer research · 2026Article
- Construction of a prognostic model and multidimensional analysis of hepatocellular carcinoma based on palmitoylation-related genes.Discover oncology · 2026Article
- FOXM1/FUS facilitates triple-negative breast cancer malignant progression and glutamine metabolism through mediating SLC7A5 transcription.Journal of molecular histology · 2026Article
- Integrative PANoptosis-focused omics analysis uncovers GSDMC as a candidate biomarker in breast cancer.Frontiers in cell and developmental biology · 2026Article
- Mathematical Modeling in Cancer Metabolism: Tools for Translational Applications in Metabolism-Based Therapy.Advances in experimental medicine and biology · 2026Review
- Prognostic Potential of Apoptosis-Related Biomarker Expression in Triple-Negative Breast Cancers.International journal of molecular sciences · 2025Article
- Analyzing Hedgehog pathway-related genes: insights into breast cancer tumor microenvironment and prognostic implications.Frontiers in immunology · 2025Article
- Polybrominated diphenyl ether profiles in adipose tissues of breast cancer patients and their carcinogenic potential investigation based on network toxicology and molecular docking.Frontiers in chemistry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundDrug resistance constitutes one of the principal causes of poor prognosis in breast cancer patients. Although cancer cells can maintain viability independently of mitochondrial energy metabolism, they remain reliant on mitochondrial functions for the synthesis of new DNA strands. This dependency underscores a potential link between mitochondrial energy metabolism and drug resistance. Hence, drug resistance and mitochondrial energy metabolism-related differentially expressed genes (DMRDEGs) may emerge as candidates for novel cancer biomarkers. This study endeavors to assess the viability of DMRDEGs as biomarkers or therapeutic targets for breast cancer.
methodsWe utilized the DRESIS database and MSigDB to identify genes related to drug resistance. Additionally, we sourced genes associated with mitochondrial energy metabolism from GeneCards and extant literature. By merging these genes with differentially expressed genes observed in normal and tumor tissues from the TCGA-BRCA and GEO databases, we successfully identified the DMRDEGs. Employing unsupervised consensus clustering, we divided breast cancer patients into two distinct groups based on the DMRDEGs. Consequently, we identified four hub genes to formulate a prognostic model, applying Cox regression, LASSO regression, and Random Forest methods. Furthermore, we examined immune infiltration and tumor mutation burden of the genes within our model and scrutinized divergences in the immune microenvironment between high- and low-risk groups. Small hairpin RNA and lentiviral plasmids were designed for stable transfection of breast cancer cell lines MDA-MB-231 and HCC1806. By conducting clone formation, scratch test, transwell assays, cell viability assay and measurement of oxygen consumption we initiated a preliminary investigation into mechanistic roles of AIFM1.
resultsWe utilized DMRDEGs to develop a prognostic model that includes four mRNAs for breast cancer. This model combined with various clinical features and critical breast cancer facets, demonstrated remarkable efficacy in predicting patient outcomes. AIFM1 appeared to enhance the proliferation, migration, and invasiveness of breast cancer cell lines MDA-MB-231 and HCC1806. Moreover, by reducing oxygen consumption, it aids in the cancer cells' acquisition of drug resistance.
conclusionsDMRDEGs hold promise as diagnostic markers and therapeutic targets for breast cancer. Among the associated mutated genes, ATP7B, FUS, AIFM1, and PPARG could serve as early diagnostic indicators, and notably, AIFM1 may present itself as a promising therapeutic target.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.