{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T01:50:30Z","timestamp":1769824230808,"version":"3.49.0"},"reference-count":72,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T00:00:00Z","timestamp":1632096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","award":["R.J130000.7851.5F156"],"award-info":[{"award-number":["R.J130000.7851.5F156"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.<\/jats:p>","DOI":"10.3390\/e23091232","type":"journal-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T07:52:14Z","timestamp":1632124334000},"page":"1232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4521-1648","authenticated-orcid":false,"given":"Hui Wen","family":"Nies","sequence":"first","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1079-4559","authenticated-orcid":false,"given":"Mohd Saberi","family":"Mohamad","sequence":"additional","affiliation":[{"name":"Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain 17666, United Arab Emirates"}]},{"given":"Zalmiyah","family":"Zakaria","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0612-3661","authenticated-orcid":false,"given":"Weng Howe","family":"Chan","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia"}]},{"given":"Muhammad Akmal","family":"Remli","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu 16100, Malaysia"}]},{"given":"Yong Hui","family":"Nies","sequence":"additional","affiliation":[{"name":"Department of Anatomy, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"i237","DOI":"10.1093\/bioinformatics\/btq182","article-title":"Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM","volume":"26","author":"Vaske","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.2147\/PPA.S143611","article-title":"Treatment decision-making among breast cancer patients in Malaysia","volume":"11","author":"Nies","year":"2017","journal-title":"Patient Prefer. Adherence"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.swevo.2016.02.002","article-title":"Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system","volume":"28","author":"Mohapatra","year":"2016","journal-title":"Swarm Evol. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1093\/bioinformatics\/btt373","article-title":"Topologically inferring risk-active pathways toward precise cancer classification by directed random walk","volume":"29","author":"Liu","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.31887\/DCNS.2004.6.1\/jpmacher2","article-title":"Treatment goals: Response and nonresponse","volume":"6","author":"Macher","year":"2004","journal-title":"Dialogues Clin. Neurosci."},{"key":"ref_6","first-page":"69","article-title":"Data Mining in Pathway Analysis for Gene Expression","volume":"Volume 9165","author":"AlAjlan","year":"2015","journal-title":"Industrial Conference on Data Mining"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yasrebi, H., Sperisen, P., Praz, V., and Bucher, P. (2009). Can Survival Prediction Be Improved by Merging Gene Expression Data Sets?. PLoS ONE, 4.","DOI":"10.1371\/journal.pone.0007431"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s10549-009-0416-z","article-title":"Data driven derivation of cutoffs from a pool of 3030 Affymetrix arrays to stratify distinct clinical types of breast cancer","volume":"120","author":"Karn","year":"2010","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1038\/nrg2884","article-title":"Analysing biological pathways in genome-wide association studies","volume":"11","author":"Wang","year":"2010","journal-title":"Nat. Rev. Genet."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/s13062-016-0152-3","article-title":"Weighted-SAMGSR: Combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes","volume":"11","author":"Tian","year":"2016","journal-title":"Biol. Direct"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, L., Ainali, C., Tsoka, S., and Papageorgiou, L.G. (2014). Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework. BMC Bioinform., 15.","DOI":"10.1186\/s12859-014-0390-2"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chan, J.H., Sootanan, P., and Larpeampaisarl, P. (August, January 31). Feature selection of pathway markers for microarray-based disease classification using negatively correlated feature sets. Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033658"},{"key":"ref_13","first-page":"676","article-title":"Pathway-based microarray analysis with negatively correlated feature sets for disease classification","volume":"Volume 7062","author":"Sootanan","year":"2011","journal-title":"International Conference on Neural Information Processing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.procs.2013.10.018","article-title":"Apriori Gene Set-based Microarray Analysis for Disease Classification Using Unlabeled Data","volume":"23","author":"Engchuan","year":"2013","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ross, A., and Willson, V.L. (2017). Basic and Advanced Statistical Tests, Sense Publishers.","DOI":"10.1007\/978-94-6351-086-8"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.jbi.2011.01.001","article-title":"An efficient statistical feature selection approach for classification of gene expression data","volume":"44","author":"Chandra","year":"2011","journal-title":"J. Biomed. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Engchuan, W., and Chan, J.H. (2012). Pathway-Based Multi-class Classification of Lung Cancer. International Conference on Neural Information Processing, Springer.","DOI":"10.1007\/978-3-642-34500-5_82"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kar, S., Das Sharma, K., and Maitra, M. (2016, January 28\u201330). A particle swarm optimization based gene identification technique for classification of cancer subgroups. Proceedings of the 2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC), Kolkata, India.","DOI":"10.1109\/CIEC.2016.7513800"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4514","DOI":"10.1007\/s00330-018-5463-6","article-title":"Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: A feasibility study","volume":"28","author":"Larroza","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_20","first-page":"132","article-title":"Selecting Genes by Test Statistics","volume":"2005","author":"Chen","year":"2005","journal-title":"J. Biomed. Biotechnol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1650015","DOI":"10.1142\/S0219720016500153","article-title":"Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach","volume":"14","author":"Engchuan","year":"2016","journal-title":"J. Bioinform. Comput. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neucom.2014.08.096","article-title":"Pathway activity transformation for multi-class classification of lung cancer datasets","volume":"165","author":"Engchuan","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1093\/bioinformatics\/bth267","article-title":"A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression","volume":"20","author":"Li","year":"2004","journal-title":"Bioinformatics"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ferdowsi, S., Voloshynovskiy, S., Gabryel, M., and Korytkowski, M. (2014). Multi-class Classification: A Coding Based Space Partitioning. International Conference on Artificial Intelligence and Soft Computing, Springer.","DOI":"10.1007\/978-3-319-07176-3_52"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jtbi.2014.06.038","article-title":"Multiclass classification of sarcomas using pathway based feature selection method","volume":"362","author":"Gu","year":"2014","journal-title":"J. Theor. Biol."},{"key":"ref_26","first-page":"477","article-title":"Multiclass cancer classification based on gene expression comparison","volume":"13","author":"Yang","year":"2014","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cmpb.2017.01.006","article-title":"Cancer subtype prediction from a pathway-level perspective by using a support vector machine based on integrated gene expression and protein network","volume":"141","author":"Hung","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1093\/bioinformatics\/btt335","article-title":"Joint network and node selection for pathway-based genomic data analysis","volume":"29","author":"Zhe","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmva.2016.12.005","article-title":"A statistical framework for pathway and gene identification from integrative analysis","volume":"156","author":"Li","year":"2017","journal-title":"J. Multivar. Anal."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1038\/nmeth.3440","article-title":"Pathway and network analysis of cancer genomes","volume":"12","author":"Creixell","year":"2015","journal-title":"Nat. Methods"},{"key":"ref_31","unstructured":"Evangeline, D.P., Sandhiya, C., Anandhakumar, P., Raj, G.D., and Rajendran, T. (2013, January 18\u201320). Feature subset selection for irrelevant data removal using Decision Tree Algorithm. Proceedings of the 2013 Fifth International Conference on Advanced Computing (ICoAC), Chennai, India."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"P2.11","DOI":"10.1186\/bcr1122","article-title":"Identification of molecular apocrine breast tumours by microarray analysis","volume":"7","author":"Farmer","year":"2005","journal-title":"Breast Cancer Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"R953","DOI":"10.1186\/bcr1325","article-title":"Gene expression profiling spares early breast cancer patients from adjuvant therapy: Derived and validated in two population-based cohorts","volume":"7","author":"Pawitan","year":"2005","journal-title":"Breast Cancer Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1093\/ije\/dyp309","article-title":"Modelling relative survival in the presence of incomplete data: A tutorial","volume":"39","author":"Nur","year":"2009","journal-title":"Int. J. Epidemiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1039\/C6MB00757K","article-title":"Topologically inferring pathway activity for precise survival outcome prediction: Breast cancer as a case","volume":"13","author":"Liu","year":"2017","journal-title":"Mol. Biosyst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85692","DOI":"10.18632\/oncotarget.21127","article-title":"Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers","volume":"8","author":"Mohammed","year":"2017","journal-title":"Oncotarget"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1093\/bioinformatics\/btl033","article-title":"A new summarization method for affymetrix probe level data","volume":"22","author":"Hochreiter","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1038\/nn.3881","article-title":"Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing","volume":"18","author":"Usoskin","year":"2015","journal-title":"Nat. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.ymeth.2014.06.004","article-title":"Degpack: A web package using a non-parametric and information theoretic algorithm to identify differentially expressed genes in multiclass RNA-seq samples","volume":"69","author":"An","year":"2014","journal-title":"Methods"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Roberts, M., and Russo, R. (2014). A Student\u2019s Guide to Analysis of Variance, Routledge.","DOI":"10.4324\/9781315787954"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"229","DOI":"10.2307\/2529724","article-title":"375: Type I Error Rates When Multiple Comparison Procedures Follow a Significant F Test of ANOVA","volume":"31","author":"Bernhardson","year":"1975","journal-title":"Biometrics"},{"key":"ref_42","first-page":"579","article-title":"Consequences of assumption violations revisited: A quantitative review of alternatives to the one-way analysis of variance F test","volume":"66","author":"Lix","year":"1996","journal-title":"Rev. Educ. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yang, R., Daigle, B.J., Petzold, L.R., and Doyle, F.J. (2012). Core module biomarker identification with network exploration for breast cancer metastasis. BMC Bioinform., 13.","DOI":"10.1186\/1471-2105-13-12"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.compbiomed.2016.08.004","article-title":"Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme","volume":"77","author":"Chan","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.ijar.2013.02.013","article-title":"Incorporating logistic regression to decision-theoretic rough sets for classifications","volume":"55","author":"Liu","year":"2014","journal-title":"Int. J. Approx. Reason."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.pmedr.2019.01.007","article-title":"Demographic, health, and attitudinal factors predictive of cancer screening decisions in older adults","volume":"13","author":"Schoenborn","year":"2019","journal-title":"Prev. Med. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Carson, M.B., and Lu, H. (2015). Network-based prediction and knowledge mining of disease genes. BMC Med. Genom., 8.","DOI":"10.1186\/1755-8794-8-S2-S9"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.gene.2013.08.027","article-title":"Insights into significant pathways and gene interaction networks underlying breast cancer cell line MCF-7 treated with 17\u03b2-Estradiol (E2)","volume":"533","author":"Huan","year":"2014","journal-title":"Gene"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhou, J., and Fu, B.-Q. (2018). The research on gene-disease association based on text-mining of PubMed. BMC Bioinform., 19.","DOI":"10.1186\/s12859-018-2048-y"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"R60","DOI":"10.1186\/gb-2003-4-9-r60","article-title":"DAVID: Database for annotation, visualization, and integrated discovery","volume":"4","author":"Dennis","year":"2003","journal-title":"Genome Biol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/nar\/gkn923","article-title":"Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists","volume":"37","author":"Huang","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nprot.2008.211","article-title":"Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources","volume":"4","author":"Huang","year":"2009","journal-title":"Nat. Protoc."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s12967-019-1790-x","article-title":"Bioinformatic gene analysis for potential biomarkers and therapeutic targets of atrial fibrillation-related stroke","volume":"17","author":"Zou","year":"2019","journal-title":"J. Transl. Med."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"495","DOI":"10.32614\/RJ-2016-062","article-title":"mctest: An R Package for Detection of Collinearity among Regressors","volume":"8","author":"Imdadullah","year":"2016","journal-title":"R J."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Enerly, E., Steinfeld, I., Kleivi, K., Leivonen, S.K., Aure, M.R., Russnes, H.G., R\u00f8nneberg, J.A., Johnsen, H., Navon, R., and R\u00f8dland, E. (2011). miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0016915"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.molonc.2015.08.002","article-title":"Serum N-glycan analysis in breast cancer patients\u2014Relation to tumour biology and clinical outcome","volume":"10","author":"Haakensen","year":"2016","journal-title":"Mol. Oncol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101683","DOI":"10.1016\/j.isci.2020.101683","article-title":"Estrogens determine adherens junction organization and E-Cadherin clustering in breast cancer cells via Amphiregulin","volume":"23","author":"Bischoff","year":"2020","journal-title":"iScience"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12943-020-01276-5","article-title":"Wnt signaling in breast cancer: Biological mechanisms, challenges and opportunities","volume":"19","author":"Xu","year":"2020","journal-title":"Mol. Cancer"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Phongwattana, T., Engchuan, W., and Chan, J.H. (2015, January 28\u201331). Clustering-based multi-class classification of complex disease. Proceedings of the 2015 7th International Conference on Knowledge and Smart Technology (KST), Chonburi, Thailand.","DOI":"10.1109\/KST.2015.7051475"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"07TR01","DOI":"10.1088\/1361-6560\/aab4b1","article-title":"Receiver operating characteristic (ROC) curves: Review of methods with applications in diagnostic medicine","volume":"63","author":"Obuchowski","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1093\/bib\/bby026","article-title":"Molecular subtyping of cancer: Current status and moving toward clinical applications","volume":"20","author":"Zhao","year":"2018","journal-title":"Brief. Bioinform."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/TMI.2015.2506270","article-title":"Machine learning methods for binary and multiclass classification of melanoma thickness from dermoscopic images","volume":"35","year":"2016","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"54","DOI":"10.7150\/jca.10631","article-title":"Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data","volume":"6","author":"Wang","year":"2015","journal-title":"J. Cancer"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"9209","DOI":"10.1073\/pnas.1201416109","article-title":"Molecular signaling network complexity is correlated with cancer patient survivability","volume":"109","author":"Breitkreutz","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_65","first-page":"293","article-title":"Overexpression of SMAR1 enhances radio-sensitivity in human breast cancer cell line MCF7 via activation of p53 signaling pathway","volume":"22","author":"Liu","year":"2015","journal-title":"Oncol. Res. Featur. Preclin. Clin. Cancer Ther."},{"key":"ref_66","first-page":"1355","article-title":"Identifying crosstalk of mTOR signaling pathway of lobular breast carcinomas","volume":"16","author":"Sun","year":"2012","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2834","DOI":"10.1002\/ijc.28315","article-title":"Loss of heterozygosity at 13q13 and 14q32 predicts BRCA2 inactivation in luminal breast carcinomas","volume":"133","author":"Popova","year":"2013","journal-title":"Int. J. Cancer"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2622","DOI":"10.1073\/pnas.0914492107","article-title":"HUNK suppresses metastasis of basal type breast cancers by disrupting the interaction between PP2A and cofilin-1","volume":"107","author":"Arpaia","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"R9","DOI":"10.1186\/bcr2814","article-title":"Enhanced RAD21 cohesin expression confers poor prognosis and resistance to chemo-therapy in high grade luminal, basal and HER2 breast cancers","volume":"13","author":"Xu","year":"2011","journal-title":"Breast Cancer Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.prp.2018.03.020","article-title":"Loss of PTEN in high grade advanced stage triple negative breast ductal cancers in African American women","volume":"214","author":"Khan","year":"2018","journal-title":"Pathol. Res. Pract."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Koni, M., Pinnar\u00f2, V., and Brizzi, M.F. (2020). The Wnt Signalling Pathway: A Tailored Target in Cancer. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21207697"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.7150\/jca.25428","article-title":"AMPH-1 is critical for breast cancer progression","volume":"9","author":"Chen","year":"2018","journal-title":"J. Cancer"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/9\/1232\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:02:27Z","timestamp":1760166147000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/9\/1232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,20]]},"references-count":72,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["e23091232"],"URL":"https:\/\/doi.org\/10.3390\/e23091232","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,20]]}}}