Skip to main content
NCBI home page
Discover Oncology logoLink to Discover Oncology
. 2026 Jan 26;17:334. doi: 10.1007/s12672-026-04500-8

Deciphering the causal nexus of mitochondrial biology with breast cancer

Dongsong Liu 1,2,#, Hongfang Xu 3,#, Yan Yan 1,2,#, Bingnan Wang 1,2, Wangjun Yan 1,2,✉
PMCID: PMC12917074  PMID: 41588143

Abstract

Background

The biological functions of mitochondria play a role in the occurrence and development of breast cancer, however, the causal relationship between them remains unclear.

Methods

Genetic instruments for mitochondrial biology and breast cancer were sourced from two genome-wide association studies (GWAS). Causal associations were assessed using Mendelian randomization analysis and validated through heterogeneity and sensitivity analyses. Additionally, the causal relationship between the identified mitochondrial proteins and the survival rate of breast cancer patients was further verified using the Kaplan-Meier Plotter.

Results

Analysis indicated causal associations between six mitochondrial proteins and breast cancer incidence. Inverse associations were found for ADP-ribose pyrophosphatase (ADPRase) and Cytochrome c oxidase subunit 8 A (COX8A), while positive correlations were observed for Pyruvate carboxylase (PC), rRNA methyltransferase 3 (MRM3), Cytochrome c oxidase assembly factor 3 homolog (COA3), and Diablo homolog (DIABLO).

Conclusion

A causal relationship between mitochondrial biology and breast cancer was established in this study. ADPRase and COX8A were identified as protective factors, whereas PC, MRM3, COA3, and DIABLO were identified as risk factors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-026-04500-8.

Keywords: Breast cancer, Mitochondrial biology, Mendelian randomization, Genetic causality

Introduction

Breast cancer stands as a formidable adversary in the realm of people’s health, commanding attention for its soaring incidence rates and the grim reaper’s share of cancer-related fatalities [1]. Delving into the molecular underpinnings of this disease reveals a tapestry of complexity, with genetic and epigenetic factors weaving a narrative of heterogeneity [2, 3]. Prominent among these factors is the emerging role of mitochondrial dysfunction in the oncogenic process, particularly within the context of breast cancer. Mitochondria, long regarded as the powerhouse of the cell, have been implicated in the quintessential hallmarks of cancer, including unbridled cell proliferation, evasion of apoptosis, and metastatic spread [4]. The interplay between breast cancer and cancer-associated adipose tissue (CAAT) shapes the malignant phenotype, and mitochondrial metabolic reprogramming plays a crucial role in this interaction, especially in the context of obesity [5].

Mitochondria are essential organelles that regulate cellular energy production, metabolism, proliferation, and apoptosis. Mitochondria have been identified as key players in the Warburg effect, where cancer cells preferentially utilize glycolysis even under aerobic conditions [6, 7]. Alterations in mitochondrial function, including changes in morphology, are one of the significant hallmarks of cancer [8]. This process is intricately linked to cellular energy balance and involves the participation of various mitochondrial proteins. Mild mitochondrial dysfunction can enhance the expansion and invasion of cancer cells, while severe dysfunction may lead to cell death, thereby inhibiting tumorigenesis. Therefore, understanding the role of mitochondrial dysfunction is crucial for the study of certain cancers, including breast cancer. Emerging evidence suggests that mitochondrial dysfunction may promote the occurrence and progression of breast cancer [9–12], but the causal relationship between mitochondrial function and breast cancer is not well established. One reason is that the methodologies employed in these studies struggle to fully account for the effects of confounding factors, which makes it difficult to distinguish between cause and effect. Therefore, a robust approach is required to conduct a comprehensive analysis of all genes associated with mitochondrial dysfunction across various types of cancer to determine whether mitochondrial dysfunction is a cause or consequence of breast cancer.

Mendelian randomization (MR) is a method that uses genetic variants as instrumental variables to infer causality in observational data, circumventing some of the biases inherent in traditional epidemiological studies [13, 14]. MR is particularly useful when randomized controlled trials are not feasible, as it can provide robust evidence concerning causal relationships between exposures and disease risks [14]. Within the framework of MR, instrumental variable selection (IVs) of single nucleotide polymorphisms (SNPs) associated only with exposure. These IVs are strategically selected for their independence from other confounding variables, enabling MR to rigorously evaluate the causal connection between the exposure in question and the resultant health outcomes [14]. MR has proven its mettle in a myriad of genetic epidemiological studies, providing insights into the causal nexus between various exposures and outcomes, including those pertinent to breast cancer risk factors [15, 16]. Establishing a causal link between mitochondrial function and breast cancer is not only crucial for the development of targeted therapeutics but also for improving the overall prognosis of breast cancer treatment. Therefore, our study will leverage MR to assess the causal associations of 66 mitochondrial proteins with breast cancer risk, which could have profound implications for the development of novel therapeutic strategies and a deeper understanding of breast cancer pathogenesis.

Methods

Data sources

The study integrated data from two comprehensive genome-wide association studies (GWAS) to elucidate the potential causal link between mitochondrial biological function and breast cancer risk. Within the analytical framework, 66 mitochondrial proteins were designated as exposure factors, with breast cancer serving as the outcome measure. Summary statistics for mitochondrial proteins were derived from a comprehensive GWAS involving 3,301 European individuals. This study analyzed a comprehensive array of 10,534,735 SNPs [17]. For breast cancer, the summary statistics were procured from the research conducted by Sakaue et al. [18]. This study included 257,730 individuals of European ancestry, comprising 17,389 cases and 240,341 controls, and analyzed a comprehensive array of 24,133,589 SNPs. The composition of this manuscript was guided by the principles outlined in the STROBEMR statement, specifically designed for MR studies [19].

Selection of IVs

IVs were selected based on genetic variants that achieved genome-wide significance, with a P-value of less than 5 × 10⁻⁶, and were associated with 66 mitochondrial proteins [20]. To reduce bias from linkage disequilibrium, the study identified independent SNPs as IVs, applying strict criteria: an r2 < 0.001, a physical distance of at least 10,000 kb, and the exclusion of palindromic sequences with frequencies in the moderate range. Additionally, to evaluate the impact of any potential weak instrument bias among the selected SNPs, we calculated and examined the F statistic. The F statistic is determined by the formula F = R2 (N - K − 1) / [K (1 - R2)], where R2 reflects the proportion of variance in the exposure explained by the SNPs, N represents the sample size, and K is the number of SNPs used in the instrument. Only those IVs with an F statistic exceeding 10 were included in the MR analysis.

Statistical analyses

In our MR analysis, we employed the inverse-variance weighted (IVW) method as the primary approach to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between mitochondria proteins and breast cancer risk. Four additional methods were also used to assess the robustness of the results. Besides, sensitivity analyses were conducted to validate the robustness of our MR findings. The MR-Egger regression method was used to assess possible horizontal pleiotropic effects, which was implicated when the intercept value differed from zero [21]. The MR pleiotropy residual sum and outlier test (MR-PRESSO) was applied to detect outlying SNPs with evidence of horizontal pleiotropy [22]. Heterogeneity across genetic variant estimates was assessed using Cochran’s Q test. Based on the outcome of this test, the IVW random-effects model was selected for the final MR analysis if the P < 0.05, indicating significant heterogeneity. Leave-one-out analysis was assessed the sensitivity of the results to individual SNPs. All statistical analyses were performed using R version 4.4.1, R Studio software, and the “Two SampleMR” and “MR-PRESSO”.

Results

IVs selection results

The number of IVs associated with the 66 mitochondrial proteins that reached the significance threshold varied from 4 to 23. The F values for all identified IVs consistently surpassed the threshold of 10, indicating a lower likelihood of weak IVs bias. This robust statistical performance underscores the strength of the IVs in our study. For a detailed examination of SNPs used as IVs were presented in detail in supplementary Tables 1–4.

Main analysis results of MR

MR analyses were performed on 66 mitochondrial proteins in relation to breast cancer. (Fig. 1). These analyses revealed suggestive evidence of causal associations between six mitochondrial proteins and the incidence of breast cancer. Specifically, MR analysis indicated a suggestive inverse association between ADP-ribose pyrophosphatase (ADPRase) and Cytochrome c oxidase subunit 8 A (COX8A) with the outcomes. Conversely, a suggestive positive correlation was observed with Pyruvate carboxylase (PC), rRNA methyltransferase 3 (MRM3), Cytochrome c oxidase assembly factor 3 homolog (COXA3) and Diablo homolog (DIABLO) with the outcomes.

Fig. 1.

Fig. 1

Open in a new tab

Circular heatmap of the MR results for the 66 identified causal effects. The horizontal coordinate represents the odds ratio value, dots depict the point estimate of odds ratio, and horizontal bars depict the 95% confidence interval. Purple represents positive effect, whereas blue represents negative effect. Red font means significant causal effect. (P < 0.05)

Sensitivity analysis

Subsequent sensitivity analyses were performed on the outcomes that showed evidence of causality. MR-Egger intercept and MR-PRESSO tests suggested the absence of horizontal pleiotropy (Fig. 2). Heterogeneity assessments, utilizing the Cochran Q statistic, showed no significant inconsistencies (Fig. 3), with all primary analyses utilizing the IVW fixed-effects model. Leave-one-out sensitivity analysis confirmed that no SNP substantially swayed the collective causal estimates (Fig. 4). These findings collectively reinforce the robustness of the causal relationships identified in our study.

Fig. 2.

Fig. 2

Open in a new tab

Forest summary map of MR estimation of the significant causal relationships and tests for heterogeneity and horizontal pleiotropy: a. ADPRase; b. PC; c. MRM3; d. COA3; e. COX8A; f. DIABLO

Fig. 3.

Fig. 3

Open in a new tab

Scatter plots of estimates for significant associations between mitochondria proteins and breast cancer. Causal influence is represented by the slope value, which is equal to the b-value computed using the five methods. A positive slope indicates a risk factor for exposure: (a) ADPRase; (b) PC; (c) MRM3; (d) COA3; (e) COX8A; (f) DIABLO

Fig. 4.

Fig. 4

Open in a new tab

Leave-one-out plots for significant associations between mitochondria proteins and breast cancer: a ADPRase; b PC; c MRM3; d COA3; e COX8A; f DIABLO

Validation of expression levels

The prognostic analysis of six mitochondrial proteins in breast cancer was investigated using the plotter tool in the Kaplan-Meier databases (Fig. 5). Survival curve analysis demonstrated that the expression levels of ADPRase and COX8A were negatively correlated with the overall survival rate of breast cancer patients, whereas the expression levels of PC, MRM3, COA3, and DIABLO were positively correlated.

Fig. 5.

Fig. 5

Open in a new tab

The correlation between mitochondrial protein expression levels and the results of Kaplan-Meier survival analysis in breast cancer patients: a. ADPRase; b. PC; c. MRM3; d. COA3; e. COX8A; f. DIABLO

Discussions

66 mitochondrial proteins and the GWAS summary statistics from European populations with breast cancer were utilized for analysis. Various MR methods, including IVW, MR Egger, Weighted Median, Simple mode and Weighted Mode, were employed to investigate the causal relationship between mitochondrial biological function and the risk of breast cancer in European populations. The IVW method was the primary approach in MR study, and the final result indicated that a causal relationship between mitochondrial biological function and the risk of breast cancer. The current study uncovered a causal link between mitochondrial biology and breast cancer, identifying ADPRase and COX8A as potential protective factors, and PC, MRM3, COA3, and DIABLO as potential risk factors. This finding was further validated by Kaplan-Meier survival curve analysis.

ADPRase, a member of the Nudix hydrolase family, is principally tasked with the hydrolysis of ADP-ribose into AMP and ribose-5-phosphate (R5P) [23]. Despite the limited research dedicated to elucidating the specific functions of ADPRase, it is noteworthy that ADP-ribose, as a product of poly ADP-ribose polymerase (RARP) enzyme activity, is implicated in a myriad of biological processes, including DNA damage repair, cellular apoptosis, and transcriptional regulation [24]. The nexus between ADP-ribose and breast cancer is predominantly observed through the therapeutic application of PARP inhibitors (PARPi). These inhibitors impede the activity of PARP enzymes, predominantly PARP1, thereby disrupting DNA repair mechanisms and precipitating the demise of cancerous cells, which translates into a therapeutic advantage in patients with breast cancer mutations [25, 26]. Clinical investigations have demonstrated that PARP inhibitors (olaparib, talazoparib, rucaparib and niraparib etc.) can markedly enhance progression-free survival and objective response rate in breast cancer patients harboring breast cancer1/2 mutations, thereby conferring significant clinical benefits [26, 27]. PARP enzymes catalyze the synthesis of ADP-ribose, while ADPRase is involved in its hydrolysis [24, 28]. This metabolic synergy suggests that the physiological role of ADPRase mirrors that of PARP inhibitors. Consistent with our results, ADPRase may constitute a potential protective element within the spectrum of breast cancer risk factors. COX8A, a subunit of Complex IV within the mitochondrial respiratory chain, plays a pivotal role in sustaining cellular oxidative phosphorylation [29]. As a key component of the oxidative phosphorylation pathway, the absence or dysfunction of COX8A can lead to mitochondrial dysfunction, thereby affecting the metabolism and survival status of tumor cells [30]. In the context of breast cancer, the loss of COX5B (another subunit of Complex IV alongside COX8A) has been shown to diminish the activity of Complex IV, leading to elevated mitochondrial reactive oxygen species (ROS) production [31]. This ROS increase can augment the proliferative and invasive capacities of tumor cells. Our study’s outcomes indicate a negative correlation between COX8A expression and the occurrence of breast cancer, potentially shedding light on the pathophysiology of breast cancer and offering a promising therapeutic target for its management.

PC is a mitochondrial enzyme that catalyzes the conversion of pyruvate to oxaloacetate, serving to replenish the tricarboxylic acid cycle [32]. Consequently, PC plays a crucial role in sustaining cellular metabolic equilibrium and modulating cellular energetics and biosynthetic pathways. In the context of oncogenic cells, these metabolic pathways are frequently reprogrammed to facilitate rapid tumorigenesis and survival [32]. The activity of PC is implicated in the metabolic reprogramming observed in tumor cells, potentially contributing to its critical role in the etiology and progression of breast cancer. Expression data from tumor patient cohorts demonstrate that PC is significantly upregulated in multiple cancerous tissues compared to their normal counterparts, with elevated levels correlating with patient prognosis [33]. Importantly, research has established that the small molecule ZY-444 targets PC, inactivating its catalytic function and thereby inhibiting the Wnt/β-catenin/Snail signaling cascade, which suppresses the growth and metastasis of breast cancer [33]. Our findings, in conjunction with these results, underscore PC as a potential contributor to the onset of breast cancer. Drawing from the current body of knowledge, MRM3 functions as a ribosomal RNA methyltransferase, modulating the stability, processing, and translation efficiency of rRNA through the methylation of specific purine residues [34]. Expression levels of MRM3 in breast tissue have been correlated with the malignancy of tumors and are predictive of patient outcomes [35]. The correlation between MRM3 and breast cancer was substantiated by our research, indicating a positive association. It is hypothesized that MRM3 may exert its influence in breast cancer by altering the methylation patterns of rRNA, which in turn affects protein synthesis and cellular proliferation. COA3, an endogenous mitochondrial protein, plays a pivotal role in the assembly of cytochrome c oxidase [36]. To date, studies have not established a connection between COA3 and the pathogenic mechanisms underlying breast cancer. However, our research potentially identifies COA3 as a target protein that bridges mitochondrial biology with breast cancer, offering new avenues for investigation into the disease’s etiology. DIABLO (a mitochondria-derived pro-apoptotic factor) promotes caspase-dependent cell death by antagonizing the inhibitory effects of the inhibitor of apoptosis proteins family [37]. Preliminary investigations suggest that DIABLO expression levels may vary among distinct subtypes of breast cancer. Specifically, protein levels of DIABLO are found to be elevated in estrogen receptor-positive breast cancers as opposed to their estrogen receptor-negative counterparts [38]. Employing IVW as our primary analytical approach, we have elucidated a potential causal link between DIABLO and breast cancer. While findings from alternative methodologies present some inconsistencies, our subsequent sensitivity analyses substantiate the robustness of our results. The variability observed may be attributed to the diverse nature of breast cancer subtypes, underscoring the importance of stratified analyses in future research endeavors.

A substantial body of theoretical evidence confirms that MR studies based on GWAS data can control for confounding factors to a certain extent in observational studies and provide accurate and stable estimates. Furthermore, potential biases can be effectively mitigated through linkage disequilibrium (LD) analysis, association analysis, weak instrument exclusion, and relevant sensitivity analyses [14, 21]. The data presented in Figs. 2 and 4 indicate the absence of heterogeneity and horizontal pleiotropy in, which further corroborates the robustness of findings. Nevertheless, this study encounters certain limitations. Firstly, our research data are predominantly focused on European populations, which may introduce racial bias. Secondly, the number of high-quality SNPs selected was relatively limited, which necessitates further validation with larger sample datasets. More importantly, while our MR analysis has revealed a causal relationship between mitochondrial biological functions and breast cancer, it remains unclear whether mitochondrial dysfunction affects the progression of breast cancer, such as metastasis. Further in-depth investigation is warranted to elucidate the potential mechanisms involved.

Conclusions

By leveraging extensive GWAS data, a causal link between mitochondrial biology and breast cancer was revealed in this study. ADPRase and COX8A were identified as potential protective factors, while PC, MRM3, COA3, and DIABLO were associated with increased risk. Further exploration of the biological mechanisms underlying these associations may inform therapeutic strategies targeting mitochondrial modulation in breast cancer treatment, thus providing a foundation for developing mitochondrial-based therapies.

Supplementary Information

Supplementary Material 1. (30KB, xls)
Supplementary Material 2. (456.1KB, xlsx)
Supplementary Material 3. (128.2KB, xlsx)
Supplementary Material 4. (188.3KB, xlsx)

Acknowledgements

We thank the authors for providing GWAS data and making the GWAS summary data publicly available. We acknowledge the participants and investigators of the IEU Open GWAS.

Author contributions

D.L., H.X. and Y.Y. wrote the main manuscript and prepared Figs. 1, 2, 3, 4 and 5. B.W. prepared supplementary Tables 1-4. W.Y. designed the study, discussed the results and reviewed the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No.82072972).

Data availability

The datasets analyzed during the present study are available in GWAS summary data (https://opengwas.io/); [Breast cancer: ebi-a-GCST90018799; 66 mitochondrial proteins traits: prot-a-63 to prot-a-3015 (detail in supplementary Table 1)]. The survival curve analyses were performed using the Kaplan-Meier plotter database (https://kmplot.com/analysis/).

Declarations

Ethics approval and consent to participate

This research used only publicly available, de-identified summary-level data (GWAS summary statistics and TCGA survival data). No new patient recruitment or intervention was undertaken; therefore, ethical approval was not required.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Dongsong Liu, Hongfang Xu and Yan Yan have contributed equally to this work.

References

  • 1.Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. [DOI] [PubMed] [Google Scholar]
  • 2.Jia G, Ping J, Guo X, et al. Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction. Nat Genet. 2024;56(5):819–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Fu X, Tan W, Song Q, Pei H, Li J. BRCA1 and breast cancer: molecular mechanisms and therapeutic strategies. Front Cell Dev Biol. 2022;10:813457. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Fang Y, Zhang Q, Guo C, et al. Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models. Front Immunol. 2024;15:1461489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Zakic T, Kalezic A, Drvendzija Z et al. Breast cancer: Mitochondria-Centered metabolic alterations in tumor and associated adipose tissue. Cells 2024, 13(2):155. [DOI] [PMC free article] [PubMed]
  • 6.Giacomello M, Pyakurel A, Glytsou C, Scorrano L. The cell biology of mitochondrial membrane dynamics. Nat Rev Mol Cell Biol. 2020;21(4):204–24. [DOI] [PubMed] [Google Scholar]
  • 7.Suomalainen A, Nunnari J. Mitochondria at the crossroads of health and disease. Cell. 2024;187(11):2601–27. [DOI] [PubMed] [Google Scholar]
  • 8.Bao X, Zhang J, Huang G, et al. The crosstalk between HIFs and mitochondrial dysfunctions in cancer development. Cell Death Dis. 2021;12(2):215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Jiang B, Wu S, Zeng L, et al. Impact of NDUFAF6 on breast cancer prognosis: linking mitochondrial regulation to immune response and PD-L1 expression. Cancer Cell Int. 2024;24(1):99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Chen K, Lu P, Beeraka NM, et al. Mitochondrial mutations and mitoepigenetics: focus on regulation of oxidative stress-induced responses in breast cancers. Semin Cancer Biol. 2022;83:556–69. [DOI] [PubMed] [Google Scholar]
  • 11.Wang M, Wei R, Li G, et al. SUMOylation of SYNJ2BP-COX16 promotes breast cancer progression through DRP1-mediated mitochondrial fission. Cancer Lett. 2022;547:215871. [DOI] [PubMed] [Google Scholar]
  • 12.She G, Du JC, Wu W, et al. Hippo pathway activation mediates chemotherapy-induced anti-cancer effect and cardiomyopathy through causing mitochondrial damage and dysfunction. Theranostics. 2023;13(2):560–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Z W, Q EL. Causal mediation analysis for time-varying heritable risk factors with Mendelian randomization. Nat Commun. 2025;16(1):6945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Sanderson E, Glymour MM, Holmes MV et al. Mendelian randomization. Nat Rev Methods Primers 2022, 2:6. [DOI] [PMC free article] [PubMed]
  • 15.Chen F, Wen W, Long J, et al. Mendelian randomization analyses of 23 known and suspected risk factors and biomarkers for breast cancer overall and by molecular subtypes. Int J Cancer. 2022;151(3):372–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Markozannes G, Kanellopoulou A, Dimopoulou O, et al. Systematic review of Mendelian randomization studies on risk of cancer. BMC Med. 2022;20(1):41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Sakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53(10):1415–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement. JAMA. 2021;326(16):1614–21. [DOI] [PubMed] [Google Scholar]
  • 20.Zhu K, Shi J, Yang R, Zhou C, Liu Z. Evidence based on Mendelian randomization: causal relationship between mitochondrial biological function and lung cancer and its subtypes. Neoplasia. 2023;46:100950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal Pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal Pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.RH HQ, BD GWMB. The ADP-ribose hydrolase NUDT5 is important for DNA repair. Cell Rep. 2022;41(12):111866. [DOI] [PubMed] [Google Scholar]
  • 24.Wang J, Wang ZQ, Zong W. ADP-ribose hydrolases: biological functions and potential therapeutic targets. Expert Rev Mol Med. 2024;26:e21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Pommier Y, O’Connor MJ, de Bono J. Laying a trap to kill cancer cells: PARP inhibitors and their mechanisms of action. Sci Transl Med. 2016;8(362):362ps317. [DOI] [PubMed] [Google Scholar]
  • 26.Taylor AM, Chan DLH, Tio M, et al. PARP (Poly ADP-Ribose Polymerase) inhibitors for locally advanced or metastatic breast cancer. Cochrane Database Syst Rev. 2021;4(4):Cd011395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Morganti S, Marra A, De Angelis C, et al. PARP inhibitors for breast cancer treatment: A review. JAMA Oncol. 2024;10(5):658–70. [DOI] [PubMed] [Google Scholar]
  • 28.DA O KG, DA B. The role of Poly-ADP ribose polymerase (PARP) enzymes in chemotherapy-induced cognitive impairments - parallels with other neurodegenerative disorders. Front Pharmacol. 2025;16:1615843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Rotko D, Kudin AP, Zsurka G, Kulawiak B, Szewczyk A, Kunz WS. Molecular and functional effects of loss of cytochrome c oxidase subunit 8A. Biochem (Mosc). 2021;86(1):33–43. [DOI] [PubMed] [Google Scholar]
  • 30.Liu YM, Ge JY, Chen YF, et al. Combined Single-Cell and Spatial transcriptomics reveal the metabolic evolvement of breast cancer during early dissemination. Adv Sci (Weinh). 2023;10(6):e2205395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Gao SP, Sun HF, Jiang HL, et al. Loss of COX5B inhibits proliferation and promotes senescence via mitochondrial dysfunction in breast cancer. Oncotarget. 2015;6(41):43363–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Kiesel VA, Sheeley MP, Coleman MF, et al. Pyruvate carboxylase and cancer progression. Cancer Metab. 2021;9(1):20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Lin Q, He Y, Wang X, et al. Targeting pyruvate carboxylase by a small molecule suppresses breast cancer progression. Adv Sci (Weinh). 2020;7(9):1903483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Nakano S, Suzuki T, Kawarada L, Iwata H, Asano K, Suzuki T. NSUN3 Methylase initiates 5-formylcytidine biogenesis in human mitochondrial tRNA(Met). Nat Chem Biol. 2016;12(7):546–51. [DOI] [PubMed] [Google Scholar]
  • 35.Zhou X, Ling Y, Cui J, et al. Mitochondrial RNA modification-based signature to predict prognosis of lower grade glioma: a multi-omics exploration and verification study. Sci Rep. 2024;14(1):12602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Watson SA, McStay GP. Functions of cytochrome c oxidase assembly factors. Int J Mol Sci 2020, 21(19):7254. [DOI] [PMC free article] [PubMed]
  • 37.Verhagen AM, Ekert PG, Pakusch M, et al. Identification of DIABLO, a mammalian protein that promotes apoptosis by binding to and antagonizing IAP proteins. Cell. 2000;102(1):43–53. [DOI] [PubMed] [Google Scholar]
  • 38.Espinosa M, Lizárraga F, Vázquez-Santillán K, et al. Coexpression of Smac/DIABLO and Estrogen receptor in breast cancer. Cancer Biomark. 2021;30(4):429–46. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (30KB, xls)
Supplementary Material 2. (456.1KB, xlsx)
Supplementary Material 3. (128.2KB, xlsx)
Supplementary Material 4. (188.3KB, xlsx)

Data Availability Statement

The datasets analyzed during the present study are available in GWAS summary data (https://opengwas.io/); [Breast cancer: ebi-a-GCST90018799; 66 mitochondrial proteins traits: prot-a-63 to prot-a-3015 (detail in supplementary Table 1)]. The survival curve analyses were performed using the Kaplan-Meier plotter database (https://kmplot.com/analysis/).

Articles from Discover Oncology are provided here courtesy of Springer

RESOURCES