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The diagnosis of BC represents a pivotal medical challenge, encompassing the application of various methods29. Presently, diagnostic approaches for BC include clinical symptom analysis, urine testing, imaging examinations, and tissue biopsies30,31. Nonetheless, these methods exhibit limitations in terms of early detection, accuracy, and invasiveness. While clinical symptom analysis and urine testing can capture potential BC symptoms and cellular information, their specificity and sensitivity need improvement to mitigate the risk of misdiagnosis or missed diagnosis. Although imaging techniques offer insights into tumor location and size, their efficacy in detecting early lesions remains constrained, often demanding prolonged time and considerable costs. Conversely, tissue biopsies, the "gold standard" for diagnosing BC, entail invasive procedures that cause patient discomfort and carry risks of complications. Furthermore, a reliable non-invasive method for early BC screening is lacking32,33. Hence, a pressing need arises to research and develop innovative technologies and methods, such as the integration of machine learning with transcriptome sequencing. This integration holds the promise to enhance the accuracy and early detection rate of BC diagnosis, ultimately offering improved medical services to patients. Overall, while the field of BC diagnosis confronts several challenges, it concurrently provides an opportunity to explore inventive diagnostic strategies and methodologies. Thus, identification of novel sensitive biomarker is very important the clinical prognosis of BC patients.

The critical role of mitochondria within cells goes beyond energy production, encompassing various biological processes, including cell survival, apoptosis, and signal transduction. Consequently, mitochondria may play a central role in tumor development, including BC. Several studies suggest a potential link between mitochondrial dysfunction and BC34,35. Tumor tissues from BC patients might exhibit abnormalities in mitochondrial function, including mitochondrial DNA mutations, alterations in mitochondrial membrane potential, and increased oxidative stress. These alterations have the potential to impair mitochondrial energy production and disrupt apoptotic pathways, thereby promoting the survival and proliferation of cancer cells. Moreover, BC progression is intricately connected to changes in metabolic pathways, which may also be associated with mitochondrial dysfunction. Some research suggests that tumor cells tend to favor glycolysis for energy production over oxidative phosphorylation. This shift in metabolic pathways, known as the " Warburg effect," could be influenced by changes in mitochondrial function6,36. In this study, we analyzed GSE13507 datasets and identified 752 DE-MRGs in BC patients. Through functional correlation analysis of 752 DE-MRGs, we have revealed their potential roles in the progression of BC. The analysis results indicated that these DE-MRGs were primarily involved in biological processes related to pattern specification, cell fate commitment, and transcription regulator complexes, which are closely associated with cell development and gene regulation. Additionally, KEGG pathway analysis has uncovered associations between these genes and neurodegenerative diseases (such as Huntington's disease, Parkinson's disease, Alzheimer's disease), cellular energy metabolism (oxidative phosphorylation), as well as metabolic pathways (such as valine, leucine, and isoleucine degradation, and the citrate cycle). Furthermore, the DO analysis indicated a correlation between these DE-MRGs and diseases such as muscular disorders, myopathy, muscle tissue diseases, and inherited metabolic disorders. In conclusion, the 752 DE-MRGs may participate in diverse biological processes and pathways during the progression of BC. These processes encompass cell development, gene regulation, energy metabolism, and neurodegenerative diseases. These findings suggested the intricate involvement of these genes in BC development, potentially influencing tumor growth, progression, metabolic anomalies, and associations with other diseases.

Machine learning combined with transcriptomic data offers several advantages in the screening of tumor biomarkers compared to traditional methods37. First, machine learning can handle high-dimensional transcriptomic data by extracting essential features to accurately identify gene expression patterns relevant to tumors. Second, machine learning can capture intricate nonlinear relationships and interactions among genes, unveiling molecular mechanisms underlying tumor development, which traditional methods may overlook. Moreover, machine learning enables personalized biomarker selection, tailoring diagnostic and treatment plans based on patients' transcriptomic data, thus enhancing precision38,39. In the realm of large-scale data analysis, machine learning's efficient processing capabilities are better equipped to uncover crucial information hidden within extensive datasets, providing timely decision support. Simultaneously, machine learning techniques rapidly generate predictive models, expediting decision-making processes with increased efficiency compared to traditional methods. Furthermore, machine learning can uncover novel biological insights, offering clues to new mechanisms of tumor development and guiding further research and therapeutic strategies40,41. Overall, the amalgamation of machine learning and transcriptomic data in tumor biomarker screening offers advantages by delivering more accurate, comprehensive, and personalized information, thereby revolutionizing tumor diagnosis and treatment. In this study, we performed LASSO and SVM-RFE, and identified four critical diagnostic genes, including GLRX2, NMT1, OXSM and TRAF3IP3. Then, we used the above four genes and developed a novel diagnostic model. Its diagnostic value was further confirmed in GSE13507, GSE3167 and GSE37816 datasets. For BC, these findings hold significant clinical implications and potential application value. Firstly, the identification of these four diagnostic genes suggests their potential pivotal role in early detection and confirmation of BC. Secondly, the development of a novel diagnostic model held the promise of providing a more precise and reliable means of diagnosing BC, thus aiding healthcare professionals in better assessing disease progression and treatment strategies. Furthermore, our findings offered substantial support for the investigation of the molecular mechanisms underpinning BC. It has the potential to uncover the latent mechanistic roles of these diagnostic genes in the progression of BC. In summary, this research paved the way for new approaches to early detection and diagnosis of BC, providing valuable insights for the advancement of precision medicine and personalized treatment.

GLRX2 is a protein closely associated with mitochondrial function and redox balance. It belongs to the glutaredoxin family of proteins, whose members exhibit redox activity within cells, aiding in the maintenance of cellular redox states and thereby sustaining normal biological functions42,43. GLRX2 is primarily localized within mitochondria, allowing it to play a crucial role in regulating mitochondrial redox balance and other mitochondrial functions. Its structural features enable it to switch between oxidized and reduced forms, participating in redox reactions. As a member of the mitochondrial glutaredoxin family, GLRX2 is involved in ensuring proper protein folding, redox state, and related biological functions within mitochondria, contributing to the maintenance of normal mitochondrial functions, including energy production processes such as ATP synthesis44,45. To data, the potential function of GLRX2 in BC was rarely reported. In this study, we found that GLRX2 was highly expressed in BC specimens. The low GLRX2 expression group exhibited an activation trend in several biological processes and diseases, including asthma, drug metabolism (via the cytochrome P450 pathway), IgA production in the intestinal immune network, xenobiotic metabolism (via the cytochrome P450 pathway), systemic lupus erythematosus, and viral myocarditis. These findings suggested a potential association between low GLRX2 expression and the aberrant activation of these biological processes, as well as the development of multiple diseases. However, further research was required to confirm specific mechanisms and interrelationships. These discoveries contributed to a deeper understanding of GLRX2's roles in biology and disease development. In addition, we found that the levels of GLRX2 were positively associated with NK cells activated and Plasma cells. The study found a positive connection between GLRX2 levels and activated NK cells as well as plasma cells, suggesting that GLRX2 might play a role in boosting NK cell activity and contributing to immune responses. Additionally, the link between GLRX2 and plasma cells hinted at its potential involvement in regulating immune reactions and inflammation. These findings could point towards GLRX2 as a potential biomarker for monitoring immune system activity and response. Further research was needed to fully comprehend the mechanisms underlying these associations.

TRAF3IP3 is a gene that encodes a protein which plays a significant role in various cellular functions, including signal transduction, apoptosis (programmed cell death), and inflammation in biological processes46,47. AIP1 typically interacts with proteins like TRAF3 (Tumor Necrosis Factor Receptor-Associated Factor 3) and RIP1 (Receptor-Interacting Protein 1), participating in the regulation of multiple signaling pathways. Among these, TRAF3 is a signaling molecule that plays a critical role in immune responses mediated by Toll-like receptors, RIG-I-like receptors, and other receptors. AIP1's interaction with TRAF3 may play an important role in regulating these immune signaling pathways48,49. Furthermore, AIP1 is believed to have a significant role in the pathway of apoptosis. Apoptosis is a programmed cell death that cells regulate to maintain the normal development and function of tissues and organs. AIP1 may influence intracellular signal transduction and impact the regulation of apoptotic pathways. In recent years, several studies have reported the potential function of TRAF3IP3 in several types of tumors. For instance, Lin et al. reported that high TRAF3IP3 levels in glioma are linked to poorer survival, possibly due to its role in promoting glioma growth through ERK signaling. TRAF3IP3 might serve as a prognostic biomarker for glioma50. However, the function of TRAF3IP3 in BC has not been investigated. In this study, we observed that TRAF3IP3 expression was distinctly decreased in BC specimens suggesting it as a tumor promotor in BC. Moreover, we found that TRAF3IP3 may play a role in regulating immune responses, antigen processing and presentation, cell adhesion, and chemokine signaling. These findings indicated that TRAF3IP3 could have significant functions in modulating immunity and cellular communication during the development of BC.

NMT1 is a gene that encodes a protein. The protein encoded by NMT1 plays a crucial role in cellular processes involving protein modification and signal transmission51,52. Belonging to the acyltransferase enzyme family, the protein produced by NMT1 is primarily responsible for attaching myristic acid molecules to amino acid residues of other proteins, a process known as N-myristoylation. This common cellular protein modification, N-myristoylation, affects protein localization, interactions, and function. Specifically, NMT1 catalyzes the N-myristoylation reaction, linking myristic acid molecules to amino acid residues of target proteins. This modification can impact various cellular processes, including signal transduction, apoptosis, and proteinprotein interactions53,54. NMT1's role in these processes is likely associated with regulating the function, stability, and localization of specific proteins. Previously, several studies have reported that NMT1 served as a tumor promotor in several tumors. For instance, Deng et al. showed that blocking N-myristoyltransferase at the genetic level breast cancer cell proliferation, migration, and invasion were all inhibited by NMT1 through the stress-activated c-Jun N-terminal kinase pathway55. In BC, elevated NMT1 expression was found to be inversely correlated with overall survival, indicating that NMT1 overexpression is associated with a poor prognosis. Moreover, increased levels of NMT1 were observed to facilitate cancer progression while simultaneously inhibiting autophagy both in vitro and in vivo56. Based on our findings, a comprehensive analysis suggested that NMT1 may have a multifaceted role in BC. Elevated NMT1 expression could be linked to interactions involving the extracellular matrix and neuroactive ligand receptor pathways, implying a potential involvement of NMT1 in tumor cell interactions with the extracellular matrix and neuro-pathways. Conversely, reduced NMT1 expression may relate to metabolic pathways (ascorbate and aldarate metabolism, starch and sucrose metabolism) and the TGF-beta signaling pathway, indicating that NMT1 might influence tumor cell metabolism and growth regulation. In this study, we also found that NMT1 was highly expressed in BC specimens and its knockdown suppressed the proliferation of BC cells, which was consistent with previous findings.

However, there were several limitations in this study. Firstly, the GEO datasets were the primary resources for our clinical data. The majority of its patients are either White, Black, or Latinx. Our results should not be generalized to patients of different races without further investigation. The current research was motivated by the statistical analysis of previously collected data; nevertheless, an optimum threshold must be established before the findings may be applied clinically. Secondly, more experiments are needed to determine the role of these essential diagnostic genes and their protein expression levels in the etiology and development of BC.

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