{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T21:52:41Z","timestamp":1781646761554,"version":"3.54.5"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Glioblastoma is an aggressive brain cancer that kills approximately one hundred thousand people worldwide every year. Unfortunately, treatment and therapy for patients with this disease are complicated and have limited efficacy in improving individuals\u2019 chances of survival. Electronic health records (EHRs) contain patient information collected routinely at hospitals through medical visits and laboratory tests, providing an interesting source of data for computational analyses. Clustering is an area of unsupervised machine learning where an algorithm partitions data according to certain statistical properties or rules, thereby identifying hidden patterns and correlations that would otherwise be difficult to notice. In this study, we applied several clustering techniques to three open datasets (Munich2019, Tainan2020, and Utrecht2019) derived from electronic health records, which included clinical, genetic, and administrative features of patients diagnosed with glioblastoma, considering two possible clusters. We evaluated our clustering results with the Density-Based Clustering Validation\u00a0(DBCV) index, a relatively new score capable of accurately assessing both convex-shaped and concave-shaped clusters. Among the methods tested, Density-based Spatial Clustering of Applications with Noise (DBSCAN) yielded the best results across all three datasets. We then analyzed the features of the clusters identified by DBSCAN and found that cytosolic Hsp70 protein in the Munich2019 dataset, sex in the Tainan2020 dataset, and brain subventricular zone in the Utrecht2019 resulted significantly capable to distinguish the two clusters.<\/jats:p>","DOI":"10.1186\/s13040-026-00549-x","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T02:23:28Z","timestamp":1774578208000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone"],"prefix":"10.1186","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9655-7142","authenticated-orcid":false,"given":"Davide","family":"Chicco","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0806-9842","authenticated-orcid":false,"given":"Srinjoy","family":"Dora","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8445-395X","authenticated-orcid":false,"given":"Luca","family":"Oneto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"549_CR1","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.biopha.2017.05.125","volume":"92","author":"K Anjum","year":"2017","unstructured":"Anjum K, Shagufta BI, Abbas SQ, Patel S, Khan I, Shah SAA, et al. Current status and future therapeutic perspectives of glioblastoma multiforme (GBM) therapy: a review. Biomed Pharmacother. 2017. 92:681\u201389. https:\/\/doi.org\/10.1016\/j.biopha.2017.05.125.","journal-title":"Biomed Pharmacother"},{"issue":"1","key":"549_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41666-023-00138-1","volume":"8","author":"G Cerono","year":"2023","unstructured":"Cerono G, Melaiu O, Chicco D. Clinical feature ranking based on ensemble machine learning reveals top survival factors for glioblastoma multiforme. J Healthc Inf Res. 2023. September;8(1):1\u201318. https:\/\/doi.org\/10.1007\/s41666-023-00138-1.","journal-title":"J Healthc Inf Res"},{"key":"549_CR3","doi-asserted-by":"publisher","unstructured":"Baheti B, Innani S, Nasrallah M, Bakas S. Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding. Front. Neurosci. 2024:18. https:\/\/doi.org\/10.3389\/fnins.2024.1304191.","DOI":"10.3389\/fnins.2024.1304191"},{"key":"549_CR4","doi-asserted-by":"publisher","unstructured":"Shen R, Qianxing M, Schultz N, Seshan VE, Olshen AB, Huse J, et al. Integrative subtype discovery in glioblastoma using iCluster. PLoS One. 2012;7(4):e35236. https:\/\/doi.org\/10.1371\/journal.pone.0035236.","DOI":"10.1371\/journal.pone.0035236"},{"issue":"11","key":"549_CR5","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1016\/j.compbiomed.2013.08.025","volume":"43","author":"JM Garc\u00eda-G\u00f3mez","year":"2013","unstructured":"Garc\u00eda-G\u00f3mez JM, G\u00f3mez-Sanchis J, Escandell-Montero P, Fuster-Garcia E, Soria-Olivas E. Sparse manifold clustering and embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors. Comput Biol Med. 2013. November;43(11):1863\u201369. https:\/\/doi.org\/10.1016\/j.compbiomed.2013.08.025.","journal-title":"Comput Biol Med"},{"key":"549_CR6","doi-asserted-by":"publisher","unstructured":"Young JD, Cai C, Xinghua L. Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma. BMC Bioinf. 2017: October;18(S11). https:\/\/doi.org\/10.1186\/s12859-017-1798-2.","DOI":"10.1186\/s12859-017-1798-2"},{"key":"549_CR7","doi-asserted-by":"publisher","unstructured":"Bhaskaran V, Nowicki MO, Idriss M, Jimenez MA, Lugli G, Hayes JL, et al. The functional synergism of microRNA clustering provides therapeutically relevant epigenetic interference in glioblastoma. Nat Commun. 2019 January;10(1). https:\/\/doi.org\/10.1038\/s41467-019-08390-z.","DOI":"10.1038\/s41467-019-08390-z"},{"key":"549_CR8","doi-asserted-by":"publisher","unstructured":"Guan X, Vengoechea J, Zheng S, Sloan AE, Chen Y, Brat DJ, et al. Molecular subtypes of glioblastoma are relevant to lower grade glioma. PLoS One. 2014 March;9(3):e91216. https:\/\/doi.org\/10.1371\/journal.pone.0091216.","DOI":"10.1371\/journal.pone.0091216"},{"issue":"2","key":"549_CR9","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1002\/jcb.29425","volume":"121","author":"Y Yang","year":"2019","unstructured":"Yang Y, Yan R, Zhang L, Meng X, Sun W. Primary glioblastoma transcriptome data analysis for screening survival-related genes. J Cellular Biochem. 2019 October;121(2):1901\u201310. https:\/\/doi.org\/10.1002\/jcb.29425.","journal-title":"J Cellular Biochem"},{"key":"549_CR10","doi-asserted-by":"publisher","unstructured":"The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008 September;455(7216):1061\u201368. https:\/\/doi.org\/10.1038\/nature07385.","DOI":"10.1038\/nature07385"},{"key":"549_CR11","doi-asserted-by":"publisher","unstructured":"Ghanem M, Ghaith AK, Zamanian C, Bon-Nieves A, Bhandarkar A, Bydon M, et al. Deep learning approaches for glioblastoma prognosis in resource-limited settings: a study using basic patient demographic, clinical, and surgical inputs. World Neurosurg. 2023 July;175:e1089\u2013109. https:\/\/doi.org\/10.1016\/j.wneu.2023.04.072.","DOI":"10.1016\/j.wneu.2023.04.072"},{"key":"549_CR12","doi-asserted-by":"publisher","unstructured":"L\u00e4mmer F, Delbridge C, W\u00fcrstle S, Neff F, Meyer B, Schlegel J, et al. Cytosolic Hsp70 as a biomarker to predict clinical outcome in patients with glioblastoma. PLoS One. 2019;14(8):e0221502. https:\/\/doi.org\/10.1371\/journal.pone.0221502.","DOI":"10.1371\/journal.pone.0221502"},{"key":"549_CR13","doi-asserted-by":"publisher","unstructured":"L\u00e4mmer F, Delbridge C, W\u00fcrstle S, Neff F, Meyer B, Schlegel J, et al. Correction: cytosolic Hsp70 as a biomarker to predict clinical outcome in patients with glioblastoma. PLoS One. 2021;16(3):e0248612. https:\/\/doi.org\/10.1371\/journal.pone.0248612.","DOI":"10.1371\/journal.pone.0248612"},{"key":"549_CR14","doi-asserted-by":"publisher","unstructured":"Shieh L-T, Guo H-R, Chung-Han H, Lin L-C, Chang C-H, Sheng-Yow H. Survival of glioblastoma treated with a moderately escalated radiation dose\u2013results of a retrospective analysis. PLoS One. 2020;15(5):e0233188. https:\/\/doi.org\/10.1371\/journal.pone.0233188.","DOI":"10.1371\/journal.pone.0233188"},{"key":"549_CR15","doi-asserted-by":"publisher","unstructured":"Berendsen S, van Bodegraven E, Seute T, Spliet WGM, Geurts M, Hendrikse J, et al. Adverse prognosis of glioblastoma contacting the subventricular zone: biological correlates. PLoS One. 2019;14(10):e0222717. https:\/\/doi.org\/10.1371\/journal.pone.0222717.","DOI":"10.1371\/journal.pone.0222717"},{"key":"549_CR16","doi-asserted-by":"publisher","unstructured":"Mark DW, Michel D, IJsbrand JA, Gabrielle A, Myles A, Arie B, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016 March;3(1). https:\/\/doi.org\/10.1038\/sdata.2016.18.","DOI":"10.1038\/sdata.2016.18"},{"key":"549_CR17","doi-asserted-by":"publisher","unstructured":"Marelli S, Chicco D. CRAN \u2013 glioblastomaEhrsdata: descriptive analysis on three EHRs datasets. 2025. https:\/\/doi.org\/10.32614\/CRAN.package.glioblastomaEHRsData.","DOI":"10.32614\/CRAN.package.glioblastomaEHRsData"},{"key":"549_CR18","doi-asserted-by":"publisher","unstructured":"Xiang X, Duan S, Pan H, Han P, Cao J, Liu C. From one-hot encoding to privacy-preserving synthetic electronic health records embedding. Proceedings of CIAT 2020 \u2013 the 2020 International Conference on Cyberspace Innovation of Advanced Technologies. 2020. p. 407\u201313. https:\/\/doi.org\/10.1145\/3444370.3444605.","DOI":"10.1145\/3444370.3444605"},{"issue":"3","key":"549_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3068335","volume":"42","author":"E Schubert","year":"2017","unstructured":"Schubert E, Sander J, Ester M, Kriegel HP, Xiaowei X. DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans Database Syst. 2017;42(3):1\u201321. https:\/\/doi.org\/10.1145\/3068335.","journal-title":"ACM Trans Database Syst"},{"key":"549_CR20","doi-asserted-by":"publisher","unstructured":"Moulavi D, Jaskowiak PA, Campello RJ, Zimek A, Sander J. Density-based clustering validation. Proceedings of SDM24 \u2013 the 2014 SIAM International Conference on Data Mining. SIAM; 2014:839\u201347: https:\/\/doi.org\/10.1137\/1.9781611973440.96.","DOI":"10.1137\/1.9781611973440.96"},{"key":"549_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/peerj-cs.3095","volume":"11","author":"D Chicco","year":"2025","unstructured":"Chicco D, Sabino G, Oneto L, Jurman G. The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters. PeerJ Comput Sci. 2025;11:1\u201337. https:\/\/doi.org\/10.7717\/peerj-cs.3095.","journal-title":"PeerJ Comput Sci"},{"key":"549_CR22","doi-asserted-by":"publisher","unstructured":"Chicco D, Oneto L, Cangelosi D. DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma. Biodata Min. 2025;18(1). https:\/\/doi.org\/10.1186\/s13040-025-00455-8.","DOI":"10.1186\/s13040-025-00455-8"},{"key":"549_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330. https:\/\/www.jmlr.org\/papers\/volume12\/pedregosa11a\/pedregosa11a.pdf.","journal-title":"J Mach Learn Res"},{"issue":"1","key":"549_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1348\/000711005X48266","volume":"59","author":"D Steinley","year":"2006","unstructured":"Steinley D. K-means clustering: a half-century synthesis. Br J Math Stat Psychol. 2006;59(1):1\u201334. https:\/\/doi.org\/10.1348\/000711005X48266.","journal-title":"Br J Math Stat Psychol"},{"key":"549_CR25","unstructured":"Andrew N, Jordan M, Weiss Y. On spectral clustering: analysis and an algorithm. Advances in neural information processing systems. 2001. https:\/\/proceedings.neurips.cc\/paper\/2001\/hash\/801272ee79cfde7fa5960571fee36b9b-Abstract.html."},{"key":"549_CR26","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/s00453-012-9717-4","volume":"69","author":"MR Ackermann","year":"2014","unstructured":"Ackermann MR, Bl\u00f6mer J, Kuntze D, Sohler C. Analysis of agglomerative clustering. Algorithmica. 2014;69:184\u2013215. https:\/\/doi.org\/10.1007\/s00453-012-9717-4.","journal-title":"Algorithmica"},{"issue":"2","key":"549_CR27","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/235968.233324","volume":"25","author":"T Zhang","year":"1996","unstructured":"Zhang T, Ramakrishnan R, Livny M. BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Rec. 1996;25(2):103\u201314. https:\/\/doi.org\/10.1145\/235968.233324.","journal-title":"ACM Sigmod Rec"},{"issue":"11","key":"549_CR28","doi-asserted-by":"publisher","first-page":"3950","DOI":"10.1016\/j.patcog.2012.04.031","volume":"45","author":"M-S Yang","year":"2012","unstructured":"Yang M-S, Lai C-Y, Lin C-Y. A robust EM clustering algorithm for Gaussian mixture models. Pattern Recognit. 2012;45(11):3950\u201361. https:\/\/doi.org\/10.1016\/j.patcog.2012.04.031.","journal-title":"Pattern Recognit"},{"key":"549_CR29","volume-title":"Affinity propagation: clustering data by passing messages","author":"D Dueck","year":"2009","unstructured":"Dueck D. Affinity propagation: clustering data by passing messages. University of Toronto; 2009 http:\/\/hdl.handle.net\/1807\/17755."},{"issue":"8","key":"549_CR30","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/34.400568","volume":"17","author":"Y Cheng","year":"1995","unstructured":"Cheng Y. Mean shift, mode seeking, and clustering. Ieee T Pattern Anal. 1995;17(8):790\u201399. https:\/\/doi.org\/10.1109\/34.400568.","journal-title":"Ieee T Pattern Anal"},{"issue":"2","key":"549_CR31","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/304181.304187","volume":"28","author":"M Ankerst","year":"1999","unstructured":"Ankerst M, Breunig MM, Kriegel H-P, Sander J. OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec. 1999;28(2):49\u201360. https:\/\/doi.org\/10.1145\/304181.304187.","journal-title":"ACM Sigmod Rec"},{"issue":"11","key":"549_CR32","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes L, Healy J, Astels S. HDBSCAN: hierarchical density based clustering. J Educ Chang Open Source Softw, 2017;2(11):205. https:\/\/doi.org\/10.21105\/joss.00205.","journal-title":"J Educ Chang Open Source Softw"},{"key":"549_CR33","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987 November;20:53\u201365. https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7.","journal-title":"J Comput Appl Math"},{"issue":"1","key":"549_CR34","doi-asserted-by":"publisher","first-page":"2826815","DOI":"10.1155\/2023\/2826815","volume":"2023","author":"G Zhang","year":"2023","unstructured":"Zhang G, Xiaolong X, Zhu L, Sisi L, Chen R, Nan L, et al. A novel molecular classification method for glioblastoma based on tumor cell differentiation trajectories. STEM Cells Int. 2023;2023(1):2826815. https:\/\/doi.org\/10.1155\/2023\/2826815.","journal-title":"STEM Cells Int"},{"key":"549_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1200\/cci.17.00080","volume":"2","author":"CA Rayfield","year":"2018","unstructured":"Rayfield CA, Grady F, De Leon G, Rockne R, Carrasco E, Jackson P, et al. Distinct phenotypic clusters of glioblastoma growth and response kinetics predict survival. JCO Clin Cancer Inf. 2018;2:1\u201314. https:\/\/doi.org\/10.1200\/cci.17.00080.","journal-title":"JCO Clin Cancer Inf"},{"issue":"23","key":"549_CR36","doi-asserted-by":"publisher","first-page":"11502","DOI":"10.1158\/0008-5472.can-06-2072","volume":"66","author":"EA Maher","year":"2006","unstructured":"Maher EA, Brennan C, Wen PY, Durso L, Ligon KL, Richardson A, et al. Marked genomic differences characterize primary and secondary glioblastoma subtypes and identify two distinct molecular and clinical secondary glioblastoma entities. Cancer Res. 2006;66(23):11502\u201313. https:\/\/doi.org\/10.1158\/0008-5472.can-06-2072.","journal-title":"Cancer Res"},{"issue":"7825","key":"549_CR37","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585(7825):357\u201362. https:\/\/doi.org\/10.1038\/s41586-020-2649-2.","journal-title":"Nature"},{"issue":"9","key":"549_CR38","first-page":"1","volume":"14","author":"W McKinney","year":"2011","unstructured":"McKinney W. Pandas: a foundational Python library for data analysis and statistics. Python High Perform Sci Comput. 2011;14(9):1\u20139. https:\/\/www.researchgate.net\/profile\/Wes-Mckinney\/publication\/265194455_pandas_a_Foundational_Python_Library_for_Data_Analysis_and_Statistics\/links\/5670827c08ae0d8b0cc0f3cc\/pandas-a-Foundational-Python-Library-for-Data-Analysis-and-Statistics.pdf.","journal-title":"Python High Perform Sci Comput"},{"key":"549_CR39","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/978-1-4842-4470-8_18","volume-title":"Introduction to scikit-learn. In building machine learning and deep learning models on Google cloud platform: a comprehensive guide for beginners","author":"E Bisong","year":"2019","unstructured":"Bisong E. Introduction to scikit-learn. In building machine learning and deep learning models on Google cloud platform: a comprehensive guide for beginners. Springer; 2019. p. 215\u201329. https:\/\/doi.org\/10.1007\/978-1-4842-4470-8_18."},{"issue":"3","key":"549_CR40","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","volume":"17","author":"P Virtanen","year":"2020","unstructured":"Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261\u201372. https:\/\/doi.org\/10.1038\/s41592-019-0686-2.","journal-title":"Nat Methods"},{"key":"549_CR41","doi-asserted-by":"publisher","unstructured":"Sandve GK, Nekrutenko A, Taylor J, Hovig E. Ten Simple rules for reproducible computational research. PLoS Comput Biol. 2013;9(10):e1003285. https:\/\/doi.org\/10.1371\/journal.pcbi.1003285.","DOI":"10.1371\/journal.pcbi.1003285"},{"issue":"4","key":"549_CR42","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1093\/jamiaopen\/ooz035","volume":"2","author":"EF Glynn","year":"2019","unstructured":"Glynn EF, Hoffman MA. Heterogeneity introduced by EHR system implementation in a de-identified data resource from 100 non-affiliated organizations. JAMIA Ppen. 2019;2(4):554\u201361. https:\/\/doi.org\/10.1093\/jamiaopen\/ooz035.","journal-title":"JAMIA Ppen"},{"issue":"4","key":"549_CR43","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1007\/s12008-020-00715-3","volume":"14","author":"MK Siddiqui","year":"2020","unstructured":"Siddiqui MK, Huang X, Morales-Menendez R, Hussain N, Khatoon K. Machine learning based novel cost-sensitive seizure detection classifier for imbalanced eeg data sets. Int J Interact Des Manuf (IJIDeM). 2020 October;14(4):1491\u2013509. https:\/\/doi.org\/10.1007\/s12008-020-00715-3.","journal-title":"Int J Interact Des Manuf (IJIDeM)"},{"key":"549_CR44","doi-asserted-by":"publisher","unstructured":"Lobinger D, Gempt J, Sievert W, Barz M, Schmitt S, Nguyen HT, et al. Potential role of Hsp70 and activated NK cells for prediction of prognosis in glioblastoma patients. Front Mol Biosci. 2021;8(669366). https:\/\/doi.org\/10.3389\/fmolb.2021.669366.","DOI":"10.3389\/fmolb.2021.669366"},{"issue":"12","key":"549_CR45","doi-asserted-by":"publisher","first-page":"3235","DOI":"10.3390\/biomedicines11123235","volume":"11","author":"P Lennartz","year":"2023","unstructured":"Lennartz P, Th\u00f6lke D, Dezfouli AB, Pilz M, Lobinger D, Messner V, et al. Biomarkers in adult-type diffuse gliomas: elevated levels of circulating vesicular heat shock protein 70 serve as a biomarker in grade 4 glioblastoma and increase NK cell frequencies in grade 3 glioma. Biomedicines. 2023;11(12):3235. https:\/\/doi.org\/10.3390\/biomedicines11123235.","journal-title":"Biomedicines"},{"key":"549_CR46","doi-asserted-by":"publisher","unstructured":"Likhomanova R, Oganesyan E, Yudintceva N, Fofanov G, Nechaeva A, Ulitin A, et al. Glioblastoma cell motility and invasion is regulated by membrane-associated heat shock protein Hsp70. J Neurooncol. 2025:1\u201311. https:\/\/doi.org\/10.1007\/s11060-025-05127-5.","DOI":"10.1007\/s11060-025-05127-5"},{"issue":"1","key":"549_CR47","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13293-024-00601-7","volume":"15","author":"B Jang","year":"2024","unstructured":"Jang B, Yoon D, Lee JY, Kim J, Hong J, Koo H, et al. Integrative multi-omics characterization reveals sex differences in glioblastoma. Biol Sex Differ. 2024;15(1):23. https:\/\/doi.org\/10.1186\/s13293-024-00601-7.","journal-title":"Biol Sex Differ"},{"key":"549_CR48","doi-asserted-by":"publisher","unstructured":"Shireman JM, Ammanuel S, Eickhoff JC, Dey M. Sexual dimorphism of the immune system predicts clinical outcomes in glioblastoma immunotherapy: a systematic review and meta-analysis. Neuro-Oncol Adv. 2022;4(1):vdac082. https:\/\/doi.org\/10.1093\/noajnl\/vdac082.","DOI":"10.1093\/noajnl\/vdac082"},{"issue":"9","key":"549_CR49","doi-asserted-by":"publisher","first-page":"2090","DOI":"10.1158\/2159-8290.cd-22-0869","volume":"13","author":"J Lee","year":"2023","unstructured":"Lee J, Nicosia M, Hong ES, Silver DJ, Cathy L, Bayik D, et al. Sex-biased T-cell exhaustion drives differential immune responses in glioblastoma. Cancer Discov. 2023;13(9):2090\u2013105. https:\/\/doi.org\/10.1158\/2159-8290.cd-22-0869.","journal-title":"Cancer Discov"},{"issue":"9","key":"549_CR50","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.3174\/ajnr.a8319","volume":"45","author":"S Gongala","year":"2024","unstructured":"Gongala S, Garcia JA, Korakavi N, Patil N, Akbari H, Sloan A, et al. Sex-specific differences in patients with IDH1\u2013wild-Type grade 4 glioma in the ReSPOND consortium. AJNR Am J Neuroradiol. 2024;45(9):1299\u2013307. https:\/\/doi.org\/10.3174\/ajnr.a8319.","journal-title":"AJNR Am J Neuroradiol"},{"issue":"7","key":"549_CR51","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.3390\/cancers16071374","volume":"16","author":"AE Barnett","year":"2024","unstructured":"Barnett AE, Ozair A, Bamashmos AS, Hong L, Bosler DS, Yeaney G, et al. MGMT methylation and differential survival impact by sex in glioblastoma. Cancers. 2024;16(7):1374. https:\/\/doi.org\/10.3390\/cancers16071374.","journal-title":"Cancers"},{"key":"549_CR52","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.radonc.2021.02.017","volume":"158","author":"K Bender","year":"2021","unstructured":"Bender K, Tr\u00e4ger M, Wahner H, Onken J, Scheel M, Beck M, et al. What is the role of the subventricular zone in radiotherapy of glioblastoma patients? Radiother Oncol. 2021;158:138\u201345. https:\/\/doi.org\/10.1016\/j.radonc.2021.02.017.","journal-title":"Radiother Oncol"},{"issue":"1","key":"549_CR53","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s12943-025-02273-2","volume":"24","author":"L Xue","year":"2025","unstructured":"Xue L, Kim HJ, Yoo J, Lee Y, Nam CH, Park J, et al. Distant origin of glioblastoma recurrence: neural stem cells in the subventricular zone serve as a source of tumor reconstruction after primary resection. Mol Cancer. 2025;24(1):64. https:\/\/doi.org\/10.1186\/s12943-025-02273-2.","journal-title":"Mol Cancer"},{"issue":"12","key":"549_CR54","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1080\/0284186x.2020.1794032","volume":"59","author":"G Hallaert","year":"2020","unstructured":"Hallaert G, Pinson H, den Broecke CV, Vanhauwaert D, Van Roost D, Boterberg T, et al. Subventricular zone contacting glioblastoma: tumor size, molecular biological factors and patient survival. Acta Oncologica. 2020;59(12):1474\u201379. https:\/\/doi.org\/10.1080\/0284186x.2020.1794032.","journal-title":"Acta Oncologica"},{"key":"549_CR55","doi-asserted-by":"publisher","unstructured":"Kahng JY, Kang B-H, Lee S-T, Choi SH, Kim TM, Park C-K, et al. Clinicogenetic characteristics and the effect of radiation on the neural stem cell niche in subventricular zone-contacting glioblastoma. Radiother Oncol. 2023;186(109800). https:\/\/doi.org\/10.1016\/j.radonc.2023.109800.","DOI":"10.1016\/j.radonc.2023.109800"},{"key":"549_CR56","doi-asserted-by":"publisher","unstructured":"Niyazi M, Andratschke N, Bendszus M, Chalmers AJ, Erridge SC, Galldiks N, et al. ESTRO-EANO guideline on target delineation and radiotherapy details for glioblastoma. Radiother Oncol. 2023;184(109663). https:\/\/doi.org\/10.1016\/j.radonc.2023.109663.","DOI":"10.1016\/j.radonc.2023.109663"},{"issue":"5","key":"549_CR57","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/0031-3203(87)90081-1","volume":"20","author":"AK Jain","year":"1987","unstructured":"Jain AK, Moreau JV. Bootstrap technique in cluster analysis. Pattern Recognit. 1987;20(5):547\u201368. https:\/\/doi.org\/10.1016\/0031-3203(87)90081-1.","journal-title":"Pattern Recognit"},{"key":"549_CR58","doi-asserted-by":"publisher","unstructured":"Beer A, Krieger L, Weber P, Ritzert M, Assent I, Plant C. DISCO: internal evaluation of density-based clustering. arXiv preprint arXiv:2503.00127, 2025. https:\/\/doi.org\/10.48550\/arXiv.2503.00127.","DOI":"10.48550\/arXiv.2503.00127"},{"key":"549_CR59","doi-asserted-by":"crossref","unstructured":"Thorsteinsdottir J, Stangl S, Peng F, Guo K, Albrecht V, Eigenbrod S, et al. Overexpression of cytosolic, plasma membrane bound and extracellular heat shock protein 70 (Hsp70) in primary glioblastomas. J Neurooncol. 2017;135:443\u201352. 10.1007\/s11060-017-2600-z.","DOI":"10.1007\/s11060-017-2600-z"},{"key":"549_CR60","doi-asserted-by":"publisher","unstructured":"Gittleman H, Ostrom QT, Stetson LC, Waite K, Hodges TR, Wright CH, et al. Sex is an important prognostic factor for glioblastoma but not for nonglioblastoma. Neurooncol Pract. 2019;6(6):451\u201362. https:\/\/doi.org\/10.1093\/nop\/npz019.","DOI":"10.1093\/nop\/npz019"},{"key":"549_CR61","doi-asserted-by":"publisher","unstructured":"Beiriger J, Habib A, Jovanovich N, Kodavali CV, Edwards L, Amankulor N, et al. The subventricular zone in glioblastoma: genesis, maintenance, and modeling. Front Oncol. 2022;12(790976). https:\/\/doi.org\/10.3389\/fonc.2022.790976.","DOI":"10.3389\/fonc.2022.790976"},{"issue":"5","key":"549_CR62","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206\u201315. https:\/\/doi.org\/10.1038\/s42256-019-0048-x.","journal-title":"Nat Mach Intell"},{"key":"549_CR63","doi-asserted-by":"publisher","unstructured":"Wani AA. Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions. PeerJ Comput Sci. 2025;11:e3025. https:\/\/doi.org\/10.7717\/peerj-cs.3025.","DOI":"10.7717\/peerj-cs.3025"},{"key":"549_CR64","doi-asserted-by":"publisher","unstructured":"Pinoli P, Chicco D, Masseroli M. Computational algorithms to predict gene ontology annotations. BMC Bioinf. 2015 April;16(S6). https:\/\/doi.org\/10.1186\/1471-2105-16-S6-S4.","DOI":"10.1186\/1471-2105-16-S6-S4"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-026-00549-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-026-00549-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-026-00549-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T20:57:33Z","timestamp":1781643453000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13040-026-00549-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["549"],"URL":"https:\/\/doi.org\/10.1186\/s13040-026-00549-x","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,27]]},"assertion":[{"value":"5 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Permission to collect and analyze the data of the patients involved in this study was obtained from the ethical committees by the original data curators, as stated in the original articles [\n                      \n                      \u2013\n                      \n                      ].","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"32"}}