{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T05:59:55Z","timestamp":1781503195934,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background: Breast cancer (BC) is one of the most diagnosed malignancies and a leading cause of cancer-related mortality among women worldwide, thereby posing a substantial threat to women\u2019s health worldwide. However, clinically robust diagnostic biomarkers with high sensitivity and specificity, as well as well-validated molecular targets for targeted therapy, remain limited. Methods: BC transcriptomic data from seven GEO datasets and the TCGA-BRCA cohort (n = 1231) were integrated for analysis. After batch-effect correction, candidate genes were screened through DEA, WGCNA, and PPI networks analysis. An ensemble machine learning (ML) framework incorporating 127 algorithmic combinations was constructed, and SHAP analysis was applied to identify hub genes. Further analyses included functional enrichment, immune infiltration, miRNA regulatory network analysis, and SMR analysis. The expression patterns were validated using single-cell transcriptome data. Drug repositioning analysis and AI-assisted virtual screening were performed to prioritize compounds with favorable drug-like properties. The predicted binding modes of candidate compounds with CHEK1 were assessed by molecular docking. Results: Thirty core genes were obtained through differential expression, WGCNA, and PPI screening. Integrated ML (127 algorithms) determined the optimal model (AUC = 0.919), and SHAP identified nine feature genes, among which CHEK1 and KIF23 showed preliminary diagnostic potential across four external cohorts (AUC: 0.625\u20130.938). Functional enrichment indicated that both are enriched in the cell cycle and p53 pathways, closely associated with BRCA1\/ATR; immune infiltration revealed significant correlations with macrophages and CD8+ T cells, with hsa-miR-15a-5p and hsa-miR-607 being common upstream regulatory miRNAs. SMR analysis supported a causal relationship between CHEK1 expression and BC genetic susceptibility (p_SMR &lt; 0.05, p_HEIDI &gt; 0.05); single-cell analysis confirms its heterogeneous expression. AI-assisted virtual screening identified 25 A-grade computational candidate compounds from 171 candidates. Molecular docking suggested that Olaparib and LY294002 can form favorable interactions with the CHEK1 active pocket. Conclusions: The study identified CHEK1 as a key diagnostic gene for BC through 127 ML algorithms and SMR causal inference. By combining AI-assisted virtual screening and molecular docking, computational candidate compounds targeting CHEK1 were prioritized. These findings represent hypothesis-generating in silico predictions and require experimental validation before any therapeutic conclusions can be drawn.<\/jats:p>","DOI":"10.3390\/genes17040396","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:20:47Z","timestamp":1774884047000},"page":"396","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrated Multi-Omics and Machine Learning Framework Identifies Diagnostic Signatures and Druggable Targets in Breast Cancer"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4166-569X","authenticated-orcid":false,"given":"Zifu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computing, Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Wilayah Persekutuan Kuala Lumpur, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0256-4373","authenticated-orcid":false,"given":"Jinqi","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, 4400 Fifth Ave, Pittsburgh, PA 15213, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computing, Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Wilayah Persekutuan Kuala Lumpur, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1938-445X","authenticated-orcid":false,"given":"Jundi","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing Equipment, China Aerospace Science and Industry Corporation Limited, Beijing 100854, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sivakumar","family":"Vengusamy","sequence":"additional","affiliation":[{"name":"School of Computing, Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Wilayah Persekutuan Kuala Lumpur, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1097\/CM9.0000000000003921","article-title":"Global burden and trends of breast cancer: GLOBOCAN 2022 estimates of incidence and mortality in 185 countries","volume":"139","author":"Gu","year":"2026","journal-title":"Chin. Med. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1016\/S0140-6736(20)32381-3","article-title":"Breast cancer","volume":"397","author":"Loibl","year":"2021","journal-title":"Lancet"},{"key":"ref_3","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.breast.2022.08.010","article-title":"Current and future burden of breast cancer: Global statistics for 2020 and 2040","volume":"66","author":"Arnold","year":"2022","journal-title":"Breast"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"565","DOI":"10.21147\/j.issn.1000-9604.2023.06.02","article-title":"Breast cancer: Epidemiology, risk factors and screening","volume":"35","author":"Xu","year":"2023","journal-title":"Chin. J. Cancer Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dvir, K., Giordano, S., and Leone, J.P. (2024). Immunotherapy in Breast Cancer. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25147517"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1186\/s12943-023-01805-y","article-title":"Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer","volume":"22","author":"Ye","year":"2023","journal-title":"Mol. Cancer"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s12943-023-01741-x","article-title":"Recent advances of small extracellular vesicle biomarkers in breast cancer diagnosis and prognosis","volume":"22","author":"Lee","year":"2023","journal-title":"Mol. Cancer"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khan, A.Q., Touseeq, M., Rehman, S., Tahir, M., Ashfaq, M., Jaffar, E., and Abbasi, S.F. (2025). Advances in breast cancer diagnosis: A comprehensive review of imaging, biosensors, and emerging wearable technologies. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1587517"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"405","DOI":"10.4048\/jbc.2023.26.e45","article-title":"Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine","volume":"26","author":"Ahn","year":"2023","journal-title":"J. Breast Cancer"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"15330338241234791","DOI":"10.1177\/15330338241234791","article-title":"Breast Cancer Prediction Based on Multiple Machine Learning Algorithms","volume":"23","author":"Zhou","year":"2024","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8487","DOI":"10.1038\/s41598-024-57740-5","article-title":"Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI","volume":"14","author":"Islam","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hussain, S., Ali, M., Naseem, U., Nezhadmoghadam, F., Jatoi, M.A., Gulliver, T.A., and Tamez-Pe\u00f1a, J.G. (2024). Breast cancer risk prediction using machine learning: A systematic review. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1343627"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s00432-025-06271-1","article-title":"The assessment of breast cancer biomarkers in diagnosis, prognosis and treatment monitoring: Integrated analysis","volume":"151","author":"Englisz","year":"2025","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7594","DOI":"10.1038\/s41598-025-85323-5","article-title":"Breast cancer prediction based on gene expression data using interpretable machine learning techniques","volume":"15","author":"Mohammed","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mirza, Z., Ansari, S., Iqbal, S., Ahmad, N., Alganmi, N., Banjar, H., Al-Qahtani, M.H., and Karim, S. (2023). Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis. Cancers, 15.","DOI":"10.3390\/cancers15123237"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gupta, S., Gupta, M.K., Shabaz, M., and Sharma, A. (2022). Deep learning techniques for cancer classification using microarray gene expression data. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.952709"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"D138","DOI":"10.1093\/nar\/gkad965","article-title":"NCBI GEO: Archive for gene expression and epigenomics data sets: 23-year update","volume":"52","author":"Clough","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"B\u00fclb\u00fcl, A., Gerdan, G., Portakal, C., Bajrami, S., and Boylu Akyerli, C. (2025). A Comprehensive Analysis of Novel Variations Associated with Bile Duct Cancer: Insights into Expression, Methylation, and 3D Protein Structure. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms262311244"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/s41591-023-02643-7","article-title":"A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer","volume":"30","author":"Amgad","year":"2024","journal-title":"Nat. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1002\/1878-0261.13550","article-title":"Transcriptional analysis of landmark molecular pathways in lung adenocarcinoma results in a clinically relevant classification with potential therapeutic implications","volume":"18","author":"Alay","year":"2024","journal-title":"Mol. Oncol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s12931-023-02647-4","article-title":"A gene-based score for the risk stratification of stage IA lung adenocarcinoma","volume":"25","author":"Xiong","year":"2024","journal-title":"Respir. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bai, L., Li, Z., Tang, C., Song, C., and Hu, F. (2025). Hypergraph-based analysis of weighted gene co-expression hypernetwork. Front. Genet., 16.","DOI":"10.3389\/fgene.2025.1560841"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s12672-025-04262-9","article-title":"Prognostic value of brown adipocyte-related genes in colorectal cancer: A multi-omics and Mendelian randomization study","volume":"17","author":"He","year":"2025","journal-title":"Discov. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1038\/s41598-019-41552-z","article-title":"Construction and Analysis of Protein-Protein Interaction Network of Heroin Use Disorder","volume":"9","author":"Chen","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e27165","DOI":"10.1016\/j.heliyon.2024.e27165","article-title":"Combining WGCNA and machine learning to identify mechanisms and biomarkers of ischemic heart failure development after acute myocardial infarction","volume":"10","author":"Li","year":"2024","journal-title":"Heliyon"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"bbag051","DOI":"10.1093\/bib\/bbag051","article-title":"Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction","volume":"27","author":"Noor","year":"2026","journal-title":"Brief. Bioinform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100034","DOI":"10.1016\/j.dche.2022.100034","article-title":"Model stacking to improve prediction and variable importance robustness for soft sensor development","volume":"3","author":"Barton","year":"2022","journal-title":"Digit. Chem. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s12243-025-01118-9","article-title":"A versatile XAI-based framework for efficient and explainable intrusion detection systems","volume":"80","author":"Nugraha","year":"2025","journal-title":"Ann. Telecommun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.21037\/tcr-21-749","article-title":"Bioinformatics analysis on enrichment analysis of potential hub genes in breast cancer","volume":"10","author":"Wei","year":"2021","journal-title":"Transl. Cancer Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"W214","DOI":"10.1093\/nar\/gkq537","article-title":"The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function","volume":"38","author":"Donaldson","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18962","DOI":"10.1038\/s41598-023-45932-4","article-title":"Machine learning-based cluster analysis of immune cell subtypes and breast cancer survival","volume":"13","author":"Wang","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"23279","DOI":"10.1038\/s41598-024-74480-8","article-title":"Bioinformatics analysis of effective biomarkers and immune infiltration in type 2 diabetes with cognitive impairment and aging","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e28162","DOI":"10.1002\/jmv.28162","article-title":"Transcriptome-wide summary data-based Mendelian randomization analysis reveals 38 novel genes associated with severe COVID-19","volume":"95","author":"Krishnamoorthy","year":"2023","journal-title":"J. Med. Virol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14755","DOI":"10.2147\/JIR.S545422","article-title":"TIMP4 as a Potential Complementary Biomarker and Therapeutic Target in Membranous Nephropathy: A Multi-Omics Investigation with Clinical Validation","volume":"18","author":"Chen","year":"2025","journal-title":"J. Inflamm. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s12672-025-01759-1","article-title":"Single-cell RNA-sequencing and genome-wide Mendelian randomisation along with abundant machine learning methods identify a novel B cells signature in gastric cancer","volume":"16","author":"Ma","year":"2025","journal-title":"Discov. Oncol."},{"key":"ref_37","first-page":"713","article-title":"Using SingleR to annotate single-cell RNA-seq data","volume":"19363","author":"Lun","year":"2020","journal-title":"DIM"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, X.-Y., Wang, S.-L., Chen, D.-H., Liu, H., You, J.-X., Su, L.-X., and Yang, X.-T. (2022). Construction and Validation of a m7G-Related Gene-Based Prognostic Model for Gastric Cancer. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.861412"},{"key":"ref_39","unstructured":"Bolcato, G. (2022). Combinare Approcci Tempo-Dipendenti e Tempo-Indipendenti Nella Scoperta di Farmaci. [Ph.D. Thesis, University of Padua]."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"W463","DOI":"10.1093\/nar\/gkaf361","article-title":"PLIP 2025: Introducing protein\u2013protein interactions to the protein\u2013ligand interaction profiler","volume":"53","author":"Schake","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kang, Y., Chang, L., Lin, H., Liu, J., Wang, C., Lu, H., and Xu, Q. (2025). Development and external validation of a machine learning-based prognostic model for small cell neuroendocrine cervical carcinoma: A multi-center study. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-15338-8"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102576","DOI":"10.1016\/j.omtn.2025.102576","article-title":"DRPM: An advanced predictive model for early diabetes detection and risk stratification","volume":"36","author":"Nie","year":"2025","journal-title":"Mol. Ther. Nucleic Acids"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rundle, S., Bradbury, A., Drew, Y., and Curtin, N.J. (2017). Targeting the ATR-CHK1 Axis in Cancer Therapy. Cancers, 9.","DOI":"10.3390\/cancers9050041"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.4161\/cc.10.8.15273","article-title":"Synthetic lethality of PARP inhibition in cancers lacking BRCA1 and BRCA2 mutations","volume":"10","author":"Dedes","year":"2011","journal-title":"Cell Cycle"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.21037\/gs-21-19","article-title":"KIF23 promotes triple negative breast cancer through activating epithelial-mesenchymal transition","volume":"10","author":"Jian","year":"2021","journal-title":"Gland Surg."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"vi18","DOI":"10.1093\/annonc\/mdy315.001","article-title":"DNA damage ATR\/Chk1 checkpoint signalling increases PD-L1 immune checkpoint activation and its implication for personalised combination therapy","volume":"29","author":"Kumar","year":"2018","journal-title":"Ann. Oncol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.1038\/s41598-020-58506-5","article-title":"Assessing diagnostic value of microRNAs from peripheral blood mononuclear cells and extracellular vesicles in Myalgic Encephalomyelitis\/Chronic Fatigue Syndrome","volume":"10","author":"Nathanson","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tozaki, Y., Aoki, H., Kato, R., Toriuchi, K., Arame, S., Inoue, Y., Hayashi, H., Kubota, E., Kataoka, H., and Aoyama, M. (2023). The Combination of ATM and Chk1 Inhibitors Induces Synthetic Lethality in Colorectal Cancer Cells. Cancers, 15.","DOI":"10.3390\/cancers15030735"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e42846","DOI":"10.1097\/MD.0000000000042846","article-title":"Predicting the ferroptosis-associated gene targets in atherosclerosis by integrating GWAS and eQTL studies summary data","volume":"104","author":"Li","year":"2025","journal-title":"Medicine"},{"key":"ref_50","unstructured":"Tomanelli, M. (2025). Unveiling Heterogeneity in Neuroblastoma Cell-Line Using Single-Cell RNA-Sequencing. [Doctoral Thesis, University of Genoa]."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rose, M., Burgess, J.T., O\u2019Byrne, K., Richard, D.J., and Bolderson, E. (2020). PARP Inhibitors: Clinical Relevance, Mechanisms of Action and Tumor Resistance. Front. Cell Dev. Biol., 8.","DOI":"10.3389\/fcell.2020.564601"},{"key":"ref_52","unstructured":"Sesma Sanz, L. (2021). Targeting DNA Repair Deficiencies with Small Molecule Drugs for Cancer Treatment. [Ph.D. Thesis, Universit\u00e9 Laval]."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Smith, H.L., Prendergast, L., and Curtin, N.J. (2020). Exploring the Synergy between PARP and CHK1 Inhibition in Matched BRCA2 Mutant and Corrected Cells. Cancers, 12.","DOI":"10.3390\/cancers12040878"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kurosu, T., Nagao, T., Wu, N., Oshikawa, G., and Miura, O. (2013). Inhibition of the PI3K\/Akt\/GSK3 Pathway Downstream of BCR\/ABL, Jak2-V617F, or FLT3-ITD Downregulates DNA Damage-Induced Chk1 Activation as Well as G2\/M Arrest and Prominently Enhances Induction of Apoptosis. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0079478"}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/17\/4\/396\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:25:34Z","timestamp":1774884334000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/17\/4\/396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":54,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["genes17040396"],"URL":"https:\/\/doi.org\/10.3390\/genes17040396","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]}}}