{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:29:03Z","timestamp":1785403743308,"version":"3.56.0"},"reference-count":67,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Recep Tayyip Erdogan University","award":["020260070010446"],"award-info":[{"award-number":["020260070010446"]}]},{"award":["020260070010446"],"award-info":[{"award-number":["020260070010446"]}],"id":[{"id":"https:\/\/ror.org\/0468j1635","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Progression-free survival (PFS) is an important clinical endpoint in Luminal A breast cancer (BC), yet the molecular determinants of recurrence remain incompletely understood. This study aimed to identify recurrence-associated transcriptomic alterations across age-defined patient subgroups and evaluate their predictive utility using machine learning (ML). Gene expression profiles and clinical data from the METABRIC cohort were analyzed in premenopausal, postmenopausal non-geriatric, and geriatric patients with Luminal A BC. Differential expression analysis identified 32 significantly dysregulated genes, of which 15 genes with |log2FC| \u2265 2 were selected for detailed characterization. Among these, KMT2D, RFNG, IGF1, and CDKN2C exhibited consistent recurrence-associated expression patterns across patient subgroups, whereas ATM, RPTOR, and RICTOR displayed subgroup-dependent expression profiles. Feature selection analysis identified KMT2D as the most influential molecular predictor, while age, NPI, and tumor size were the most important clinical variables. The integrated clinicopathological\u2013transcriptomic XGBoost model achieved the best predictive performance (AUC = 0.71; accuracy = 0.78) and outperformed baseline models based only on clinicopathological variables. Sensitivity analysis and independent external validation supported the robustness of the principal candidate biomarkers. These findings provide preliminary evidence supporting the investigation of transcriptomic biomarkers in combination with clinical variables for recurrence classification in Luminal A BC. However, further validation in independent cohorts is needed before their potential utility can be fully assessed.<\/jats:p>","DOI":"10.3390\/biology15141160","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:05:07Z","timestamp":1784163907000},"page":"1160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Age-Specific Transcriptomic Signatures for Classification of Progression-Free Survival Outcomes in Luminal A Breast Cancer: An Integrative Machine Learning Approach"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2405-8552","authenticated-orcid":false,"given":"Mehmet","family":"Kivrak","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, Faculty of Medicine, Recep Tayyip Erdogan University, 53020 Rize, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7246-2592","authenticated-orcid":false,"given":"Ihsan","family":"Nalkiran","sequence":"additional","affiliation":[{"name":"Department of Medical Biology, Faculty of Medicine, Recep Tayyip Erdogan University, 53020 Rize, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1115-2005","authenticated-orcid":false,"given":"Hatice","family":"Sevim Nalkiran","sequence":"additional","affiliation":[{"name":"Department of Medical Biology, Faculty of Medicine, Recep Tayyip Erdogan University, 53020 Rize, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1002\/ijc.31937","article-title":"Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods","volume":"144","author":"Ferlay","year":"2019","journal-title":"Int. J. Cancer"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1002\/path.2808","article-title":"The molecular pathology of breast cancer progression","volume":"223","author":"Bombonati","year":"2011","journal-title":"J. Pathol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wu, H.J., and Chu, P.Y. (2022). Current and Developing Liquid Biopsy Techniques for Breast Cancer. Cancers, 14.","DOI":"10.3390\/cancers14092052"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1053\/j.sult.2017.09.006","article-title":"The Role of Ultrasound in Breast Cancer Screening: The Case for and Against Ultrasound","volume":"39","author":"Geisel","year":"2018","journal-title":"Semin. Ultrasound CT MR"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lim, Y.X., Lim, Z.L., Ho, P.J., and Li, J. (2022). Breast Cancer in Asia: Incidence, Mortality, Early Detection, Mammography Programs, and Risk-Based Screening Initiatives. Cancers, 14.","DOI":"10.20944\/preprints202208.0105.v1"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1056\/NEJMoa1701830","article-title":"20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years","volume":"377","author":"Pan","year":"2017","journal-title":"N. Engl. J. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"99","DOI":"10.3111\/13696998.2013.864973","article-title":"Economic outcomes of sequences which include monoclonal antibodies against vascular endothelial growth factor and\/or epidermal growth factor receptor for the treatment of unresectable metastatic colorectal cancer","volume":"17","author":"Rautenberg","year":"2014","journal-title":"J. Med. Econ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1056\/NEJMoa052306","article-title":"Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer","volume":"353","author":"Procter","year":"2005","journal-title":"N. Engl. J. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1001\/jama.2018.19323","article-title":"Breast Cancer Treatment: A Review","volume":"321","author":"Waks","year":"2019","journal-title":"JAMA"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Phung, M.T., Tin Tin, S., and Elwood, J.M. (2019). Prognostic models for breast cancer: A systematic review. BMC Cancer, 19.","DOI":"10.1186\/s12885-019-5442-6"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"292","DOI":"10.4048\/jbc.2016.19.3.292","article-title":"Associations between Dietary Allium Vegetables and Risk of Breast Cancer: A Hospital-Based Matched Case-Control Study","volume":"19","author":"Pourzand","year":"2016","journal-title":"J. Breast Cancer"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27586","DOI":"10.18632\/oncotarget.25496","article-title":"Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer","volume":"9","author":"Arevalillo","year":"2018","journal-title":"Oncotarget"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: Past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. Neurol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.canlet.2019.12.007","article-title":"Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges","volume":"471","author":"Huang","year":"2020","journal-title":"Cancer Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1001\/jama.2013.393","article-title":"The inevitable application of big data to health care","volume":"309","author":"Murdoch","year":"2013","journal-title":"JAMA"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1089\/omi.2016.0077","article-title":"How Healthcare Can Refocus on Its Super-Customers (Patients, n \u2009= 1) and Customers (Doctors and Nurses) by Leveraging Lessons from Amazon, Uber, and Watson","volume":"20","author":"Kolker","year":"2016","journal-title":"Omics"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Daimiel Naranjo, I., Gibbs, P., Reiner, J.S., Lo Gullo, R., Sooknanan, C., Thakur, S.B., Jochelson, M.S., Sevilimedu, V., Morris, E.A., and Baltzer, P.A.T. (2021). Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis. Diagnostics, 11.","DOI":"10.3390\/diagnostics11060919"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2292","DOI":"10.1093\/annonc\/mdt183","article-title":"A simple and reproducible prognostic index in luminal ER-positive breast cancers","volume":"24","author":"Castellano","year":"2013","journal-title":"Ann. Oncol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.1158\/1078-0432.CCR-22-0619","article-title":"Clinical Validation of EndoPredict in Pre-Menopausal Women with ER-Positive, HER2-Negative Primary Breast Cancer","volume":"28","author":"Constantinidou","year":"2022","journal-title":"Clin. Cancer Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1158\/1078-0432.CCR-14-2053","article-title":"A novel MAPK-microRNA signature is predictive of hormone-therapy resistance and poor outcome in ER-positive breast cancer","volume":"21","author":"Miller","year":"2015","journal-title":"Clin. Cancer Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1158\/1078-0432.CCR-16-0362","article-title":"Estrogen Receptor Binding (18F-FES PET) and Glycolytic Activity (18F-FDG PET) Predict Progression-Free Survival on Endocrine Therapy in Patients with ER+ Breast Cancer","volume":"23","author":"Kurland","year":"2017","journal-title":"Clin. Cancer Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nalkiran, I., Sevim Nalkiran, H., Ozcelik, N., and Kivrak, M. (2025). In Silico Identification of LSD1 Inhibition-Responsive Targets in Small Cell Lung Cancer. Bioengineering, 12.","DOI":"10.3390\/bioengineering12050504"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2817","DOI":"10.1056\/NEJMoa041588","article-title":"A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer","volume":"351","author":"Paik","year":"2004","journal-title":"N. Engl. J. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"Dai","year":"2002","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e515","DOI":"10.1016\/S2589-7500(23)00095-X","article-title":"Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: A discovery and validation study","volume":"5","author":"Gui","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","article-title":"Pan-cancer integrative histology-genomic analysis via multimodal deep learning","volume":"40","author":"Chen","year":"2022","journal-title":"Cancer Cell"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1038\/s43018-022-00388-9","article-title":"Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer","volume":"3","author":"Boehm","year":"2022","journal-title":"Nat. Cancer"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41586-021-04278-5","article-title":"Multi-omic machine learning predictor of breast cancer therapy response","volume":"601","author":"Sammut","year":"2022","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41568-021-00408-3","article-title":"Harnessing multimodal data integration to advance precision oncology","volume":"22","author":"Boehm","year":"2022","journal-title":"Nat. Rev. Cancer"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kivrak, M., Sevim Nalkiran, H., Kesen, O., and Nalkiran, I. (2025). Integrative Machine Learning Model for Overall Survival Prediction in Breast Cancer Using Clinical and Transcriptomic Data. Biology, 14.","DOI":"10.3390\/biology14111539"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1001\/jamaoncol.2015.0373","article-title":"Five Years of Cancer Drug Approvals: Innovation, Efficacy, and Costs","volume":"1","author":"Mailankody","year":"2015","journal-title":"JAMA Oncol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1634\/theoncologist.2015-0366","article-title":"Clinical Implications of the Progression-Free Survival Endpoint for Treatment of Hormone Receptor-Positive Advanced Breast Cancer","volume":"21","author":"Kaklamani","year":"2016","journal-title":"Oncologist"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104715","DOI":"10.1016\/j.breast.2026.104715","article-title":"Predicting progression-free survival in hormone-receptor positive (HR+\/HER2\u2212) metastatic breast cancer (MBC) treated with CDK4\/6 inhibitors: A machine learning approach","volume":"86","author":"Pannunzio","year":"2026","journal-title":"Breast"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Taghizadeh, E., Heydarheydari, S., Saberi, A., JafarpoorNesheli, S., and Rezaeijo, S.M. (2022). Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04965-8"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/s41591-022-02160-z","article-title":"The next generation of evidence-based medicine","volume":"29","author":"Subbiah","year":"2023","journal-title":"Nat. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1038\/nature10983","article-title":"The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups","volume":"486","author":"Curtis","year":"2012","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kivrak, M., Nalkiran, I., and Sevim Nalkiran, H. (2025). Exploring the Therapeutic Potential of the DOT1L Inhibitor EPZ004777 Using Bioinformatics and Molecular Docking Approaches in Acute Myeloid Leukemia. Curr. Issues Mol. Biol., 47.","DOI":"10.3390\/cimb47030173"},{"key":"ref_39","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). FrontMatter, John Wiley & Sons, Inc.. [3rd ed.]."},{"key":"ref_40","unstructured":"Haykin, S. (2009). Neural Networks and Learning Machines, 3\/E, Pearson Education India."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. KDD \u201916: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e01503","DOI":"10.7554\/eLife.01503","article-title":"H3K4 mono- and di-methyltransferase MLL4 is required for enhancer activation during cell differentiation","volume":"2","author":"Lee","year":"2013","journal-title":"eLife"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"168453","DOI":"10.1016\/j.jmb.2024.168453","article-title":"KMT2 Family of H3K4 Methyltransferases: Enzymatic Activity-dependent and -independent Functions","volume":"436","author":"Van","year":"2024","journal-title":"J. Mol. Biol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8513","DOI":"10.1073\/pnas.0902873106","article-title":"A tumor suppressive coactivator complex of p53 containing ASC-2 and histone H3-lysine-4 methyltransferase MLL3 or its paralogue MLL4","volume":"106","author":"Lee","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e7","DOI":"10.1016\/S1470-2045(20)30556-8","article-title":"The genomic landscape of breast cancer brain metastases: A systematic review","volume":"22","author":"Morgan","year":"2021","journal-title":"Lancet Oncol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1146\/annurev-pathol-052016-100127","article-title":"The Varied Roles of Notch in Cancer","volume":"12","author":"Aster","year":"2017","journal-title":"Annu. Rev. Pathol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.ijbiomac.2022.09.203","article-title":"Non-coding RNAs targeting notch signaling pathway in cancer: From proliferation to cancer therapy resistance","volume":"222","author":"Hashemi","year":"2022","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/ng0797-283","article-title":"Fringe boundaries coincide with Notch-dependent patterning centres in mammals and alter Notch-dependent development in Drosophila","volume":"16","author":"Cohen","year":"1997","journal-title":"Nat. Genet."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"42454","DOI":"10.1074\/jbc.M509552200","article-title":"Lunatic fringe, manic fringe, and radical fringe recognize similar specificity determinants in O-fucosylated epidermal growth factor-like repeats","volume":"280","author":"Rampal","year":"2005","journal-title":"J. Biol. Chem."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gong, X., Zheng, C., Jia, H., Liu, Y., Yang, R., Chen, Z., Pan, Y., Li, X., and Liu, Y. (2023). A pan-cancer analysis revealing the role of LFNG, MFNG and RFNG in tumor prognosis and microenvironment. BMC Cancer, 23.","DOI":"10.1186\/s12885-023-11545-3"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kotsifaki, A., Maroulaki, S., Karalexis, E., Stathaki, M., and Armakolas, A. (2024). Decoding the Role of Insulin-like Growth Factor 1 and Its Isoforms in Breast Cancer. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25179302"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111111","DOI":"10.1016\/j.biopha.2020.111111","article-title":"Progress of exosomes in the diagnosis and treatment of lung cancer","volume":"134","author":"Xu","year":"2021","journal-title":"BioMed Pharmacother."},{"key":"ref_55","first-page":"409","article-title":"Downregulated miRNA-22-3p promotes the progression and leads to poor prognosis of hepatocellular carcinoma through targeting CDKN2C","volume":"26","author":"Kong","year":"2021","journal-title":"J. Buon"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1002\/2211-5463.12819","article-title":"MiR-21-5p promotes cell proliferation and G1\/S transition in melanoma by targeting CDKN2C","volume":"10","author":"Yang","year":"2020","journal-title":"FEBS Open Bio"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.3389\/fonc.2020.01546","article-title":"LINC00673 Represses CDKN2C and Promotes the Proliferation of Esophageal Squamous Cell Carcinoma Cells by EZH2-Mediated H3K27 Trimethylation","volume":"10","author":"Zhou","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_58","first-page":"3161","article-title":"PRMT6 serves an oncogenic role in lung adenocarcinoma via regulating p18","volume":"22","author":"Tang","year":"2020","journal-title":"Mol. Med. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Phan, L.M., and Rezaeian, A.-H. (2021). ATM: Main Features, Signaling Pathways, and Its Diverse Roles in DNA Damage Response, Tumor Suppression, and Cancer Development. Genes, 12.","DOI":"10.3390\/genes12060845"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.semcdb.2014.09.011","article-title":"Growing knowledge of the mTOR signaling network","volume":"36","author":"Huang","year":"2014","journal-title":"Semin. Cell Dev. Biol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.cell.2012.03.017","article-title":"MTOR signaling in growth control and disease","volume":"149","author":"Laplante","year":"2012","journal-title":"Cell"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S0092-8674(02)00833-4","article-title":"Raptor, a binding partner of target of rapamycin (TOR), mediates TOR action","volume":"110","author":"Hara","year":"2002","journal-title":"Cell"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0092-8674(02)00808-5","article-title":"MTOR interacts with raptor to form a nutrient-sensitive complex that signals to the cell growth machinery","volume":"110","author":"Kim","year":"2002","journal-title":"Cell"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1038\/ncb1183","article-title":"Mammalian TOR complex 2 controls the actin cytoskeleton and is rapamycin insensitive","volume":"6","author":"Jacinto","year":"2004","journal-title":"Nat. Cell Biol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1016\/j.cub.2004.06.054","article-title":"Rictor, a novel binding partner of mTOR, defines a rapamycin-insensitive and raptor-independent pathway that regulates the cytoskeleton","volume":"14","author":"Sarbassov","year":"2004","journal-title":"Curr. Biol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1002\/cam4.2810","article-title":"RAPTOR promotes colorectal cancer proliferation by inducing mTORC1 and upregulating ribosome assembly factor URB1","volume":"9","author":"Wang","year":"2020","journal-title":"Cancer Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4752","DOI":"10.1158\/0008-5472.CAN-15-3393","article-title":"Rictor\/mTORC2 Drives Progression and Therapeutic Resistance of HER2-Amplified Breast Cancers","volume":"76","author":"Hicks","year":"2016","journal-title":"Cancer Res."}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/15\/14\/1160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:24:38Z","timestamp":1784165078000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/15\/14\/1160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"references-count":67,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["biology15141160"],"URL":"https:\/\/doi.org\/10.3390\/biology15141160","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]}}}