{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T23:58:55Z","timestamp":1769558335236,"version":"3.49.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"S6","license":[{"start":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T00:00:00Z","timestamp":1678060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T00:00:00Z","timestamp":1678060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003852","name":"Regione Campania","doi-asserted-by":"publisher","award":["SATIN-POR CAMPANIA FESR 2014\/2020"],"award-info":[{"award-number":["SATIN-POR CAMPANIA FESR 2014\/2020"]}],"id":[{"id":"10.13039\/501100003852","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Recent studies have indicated that a special class of long non-coding RNAs (lncRNAs), namely Transcribed-Ultraconservative Regions are transcribed from specific DNA regions (T-UCRs), 100<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> conserved in human, mouse, and rat genomes. This is noticeable, as lncRNAs are usually poorly conserved. Despite their peculiarities, T-UCRs remain very understudied in many diseases, including cancer and, yet, it is known that dysregulation of T-UCRs is associated with cancer as well as with human neurological, cardiovascular, and developmental pathologies. We have recently reported the T-UCR uc.8+ as a potential prognostic biomarker in bladder cancer.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The aim of this work is to develop a methodology, based on machine learning techniques, for the selection of a predictive signature panel for bladder cancer onset. To this end, we analyzed the expression profiles of T-UCRs from surgically removed normal and bladder cancer tissues, by using custom expression microarray. Bladder tissue samples from 24 bladder cancer patients (12 Low Grade and 12 High Grade), with complete clinical data, and 17 control samples from normal bladder epithelium were analysed. After the selection of preferentially expressed and statistically significant T-UCRs, we adopted an ensemble of statistical and machine learning based approaches (i.e., logistic regression, Random Forest, XGBoost and LASSO) for ranking the most important diagnostic molecules. We identified a signature panel of 13 selected T-UCRs with altered expression profiles in cancer, able to efficiently discriminate between normal and bladder cancer patient samples. Also, using this signature panel, we classified bladder cancer patients in four groups, each characterized by a different survival extent. As expected, the group including only Low Grade bladder cancer patients had greater overall survival than patients with the majority of High Grade bladder cancer. However, a specific signature of deregulated T-UCRs identifies sub-types of bladder cancer patients with different prognosis regardless of the bladder cancer Grade.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Here we present the results for the classification of bladder cancer (Low and High Grade) patient samples and normal bladder epithelium controls by using a machine learning application. The T-UCR\u2019s panel can be used for learning an eXplainable Artificial Intelligent model and develop a robust decision support system for bladder cancer early diagnosis providing urinary T-UCRs data of new patients. The use of this system instead of the current methodology will result in a non-invasive approach, reducing uncomfortable procedures (such as cystoscopy) for the patients. Overall, these results raise the possibility of new automatic systems, which could help the RNA-based prognosis and\/or the cancer therapy in bladder cancer patients, and demonstrate the successful application of Artificial Intelligence to the definition of an independent prognostic biomarker panel.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-023-05167-6","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T10:12:39Z","timestamp":1678097559000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology"],"prefix":"10.1186","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5592-7995","authenticated-orcid":false,"given":"Angelo","family":"Ciaramella","sequence":"first","affiliation":[]},{"given":"Emanuel","family":"Di Nardo","sequence":"additional","affiliation":[]},{"given":"Daniela","family":"Terracciano","sequence":"additional","affiliation":[]},{"given":"Lia","family":"Conte","sequence":"additional","affiliation":[]},{"given":"Ferdinando","family":"Febbraio","sequence":"additional","affiliation":[]},{"given":"Amelia","family":"Cimmino","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,6]]},"reference":[{"issue":"5","key":"5167_CR1","first-page":"24818","volume":"14","author":"W Zhong","year":"2022","unstructured":"Zhong W, Qu H, Yao B, Wang D, Qiu J. Analysis of a long non-coding RNA associated signature to predict survival in patients with bladder cancer. Cureus. 2022;14(5):24818.","journal-title":"Cureus"},{"issue":"6","key":"5167_CR2","doi-asserted-by":"publisher","first-page":"152429","DOI":"10.1016\/j.prp.2019.04.021","volume":"215","author":"W Zhu","year":"2019","unstructured":"Zhu W, Liu H, Wang X, Lu J, Yang W. Long noncoding RNAs in bladder cancer prognosis: a meta-analysis. Pathol Res Pract. 2019;215(6):152429.","journal-title":"Pathol Res Pract"},{"key":"5167_CR3","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1126\/science.1098119","volume":"304","author":"G Bejerano","year":"2004","unstructured":"Bejerano G, Pheasant M, Makunin I, Stephen S, Kent WJ, Mattick JS, Haussler D. Ultraconserved elements in the human genome. Science. 2004;304:1321\u20135.","journal-title":"Science"},{"key":"5167_CR4","doi-asserted-by":"crossref","unstructured":"Olivieri M, Ferro M, Terreri S, Durso M, Romanelli A, Avitabile C, De\u00a0Cobelli O, Messere A, Bruzzese D, Vannini I, Marinelli L, Novellino E, Zhang W, Incoronato M, Ilardi G, Staibano S, Marra L, Franco R, Perdon$$\\grave{\\text{a}}$$ S, Terracciano D, Czerniak B, Liguori G, Colonna V, Fabbri M, Febbraio F, Calin G, Cimmino A. Long non-coding RNA containing ultraconserved genomic region 8 promotes bladder cancer tumorigenesis. Oncotarget. 2016;7:20636\u201354.","DOI":"10.18632\/oncotarget.7833"},{"key":"5167_CR5","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.ccr.2007.07.027","volume":"12","author":"G Calin","year":"2007","unstructured":"Calin G, Liu C, Ferracin M, Hyslop T, Spizzo R, Sevignani C, Fabbri M, Cimmino A, et al. Ultraconserved regions encoding ncRNAs are altered in human leukemias and carcinomas. Cancer Cell. 2007;12:215\u201329.","journal-title":"Cancer Cell"},{"key":"5167_CR6","first-page":"1","volume":"681","author":"S Terreri","year":"2021","unstructured":"Terreri S, et al. Subcellular localization of uc8+ as a prognostic biomarker in bladder cancer tissue. Cancers. 2021;681:1\u201319.","journal-title":"Cancers"},{"key":"5167_CR7","first-page":"15","volume":"8","author":"K Saginala","year":"2020","unstructured":"Saginala K, Barsouk A, Aluru JS, Rawla P, Padala S, Barsouk A. Epidemiology of bladder cancer. Med Sci. 2020;8:15.","journal-title":"Med Sci"},{"key":"5167_CR8","doi-asserted-by":"publisher","first-page":"296","DOI":"10.2174\/157489310794072508","volume":"5","author":"P Yang","year":"2010","unstructured":"Yang P, Hwa Yang Y, Zhou B, Zomaya A. A review of ensemble methods in bioinformatics. Curr Bioinform. 2010;5:296\u2013308.","journal-title":"Curr Bioinform"},{"key":"5167_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-9-319","volume":"9","author":"A Statnikov","year":"2008","unstructured":"Statnikov A, Wang L, Aliferis CF. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinform. 2008;9:1\u201310.","journal-title":"BMC Bioinform"},{"key":"5167_CR10","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.csda.2004.03.017","volume":"48","author":"JW Lee","year":"2005","unstructured":"Lee JW, Lee JB, Park M, Song SH. An extensive comparison of recent classification tools applied to microarray data. Comput Stat Data Anal. 2005;48:869\u201385.","journal-title":"Comput Stat Data Anal"},{"key":"5167_CR11","first-page":"41","volume":"15","author":"S Huang","year":"2018","unstructured":"Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics. 2018;15:41\u201351.","journal-title":"Cancer Genomics Proteomics"},{"key":"5167_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"5167_CR13","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23:2507\u201317.","journal-title":"Bioinformatics"},{"key":"5167_CR14","doi-asserted-by":"crossref","unstructured":"Iuliano A, Occhipinti A, Angelini C, De\u00a0Feis I, Li$$\\grave{\\text{ o }}$$ P. Combining pathway identification and breast cancer survival prediction via screening-network methods. Front Genet. 2018;9:200\u20136.","DOI":"10.3389\/fgene.2018.00206"},{"key":"5167_CR15","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189\u2013232.","journal-title":"Ann Stat"},{"issue":"40","key":"5167_CR16","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.21105\/joss.01317","volume":"4","author":"C Davidson-Pilon","year":"2019","unstructured":"Davidson-Pilon C. Lifelines: survival analysis in python. J Open Source Softw. 2019;4(40):1317. https:\/\/doi.org\/10.21105\/joss.01317.","journal-title":"J Open Source Softw"},{"issue":"350","key":"5167_CR17","first-page":"1","volume":"21","author":"A Ciaramella","year":"2020","unstructured":"Ciaramella A, Nardone D, Staiano A. Data integration by fuzzy similarity-based hierarchical clustering. BMC Bioinform. 2020;21(350):1\u201315.","journal-title":"BMC Bioinform"},{"key":"5167_CR18","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-319-33747-0_17","volume":"54","author":"A Ciaramella","year":"2016","unstructured":"Ciaramella A, Staiano A, Cervone G, Alessandrini S. A bayesian-based neural network model for solar photovoltaic power forecasting. Smart Innov Syst Technol. 2016;54:169\u201377.","journal-title":"Smart Innov Syst Technol"},{"key":"5167_CR19","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.ecoinf.2018.12.001","volume":"49","author":"E Chianese","year":"2019","unstructured":"Chianese E, Camastra F, Ciaramella A, Landi TC, Staiano A, Riccio A. Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron. Ecol Inform. 2019;49:54\u201361.","journal-title":"Ecol Inform"},{"key":"5167_CR20","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1002\/bab.2249","volume":"69","author":"D Nardone","year":"2021","unstructured":"Nardone D, Ciaramella A, Cerreta M, Pulcrano S, Bellenchi G, Leone L, Manco G, Febbraio F. Selymatra: a web application for protein-profiling analysis of mass spectra. Biotechnol Appl Biochem. 2021;69:1821\u20139.","journal-title":"Biotechnol Appl Biochem"},{"key":"5167_CR21","doi-asserted-by":"publisher","first-page":"1231003","DOI":"10.1142\/S0219720012310038","volume":"10","author":"W Li","year":"2012","unstructured":"Li W. Volcano plots in analyzing differential expressions with mRNA microarrays. J Bioinform Comput Biol. 2012;10:1231003.","journal-title":"J Bioinform Comput Biol."},{"key":"5167_CR22","unstructured":"Hooker S, Erhan D, Kindermans P, Kim B. Evaluating feature importance estimates. 2018."},{"key":"5167_CR23","unstructured":"Liu H, Motoda H, Yu L. Feature selection with selective sampling. In: Proceedings of the nineteenth international conference on machine learning. Citeseer. 2002."},{"issue":"3","key":"5167_CR24","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/34.990133","volume":"24","author":"P Mitra","year":"2002","unstructured":"Mitra P, Murthy C, Pal SK. Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell. 2002;24(3):301\u201312.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5167_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-2445-5","volume-title":"Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications","author":"K Srinivasa","year":"2020","unstructured":"Srinivasa K, Siddesh G, Manisekhar S. Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications. Berlin: Springer; 2020."},{"key":"5167_CR26","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM. Pattern recognition and machine learning. Cambridge: Springer; 2006."},{"key":"5167_CR27","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.237","volume":"5","author":"D Nardone","year":"2019","unstructured":"Nardone D, Ciaramella A, Staiano A. A sparse-modeling based approach for class specific feature selection. Peerj Comput Sci. 2019;5: e237.","journal-title":"Peerj Comput Sci"},{"key":"5167_CR28","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016. pp. 785\u2013794.","DOI":"10.1145\/2939672.2939785"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05167-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-023-05167-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05167-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T10:13:29Z","timestamp":1678097609000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-023-05167-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,6]]},"references-count":28,"journal-issue":{"issue":"S6","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["5167"],"URL":"https:\/\/doi.org\/10.1186\/s12859-023-05167-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,6]]},"assertion":[{"value":"8 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of University of Naples \u201cFederico II\u201d (protocol code with Prot. N. 235\/20, 8 July 2020). Informed consent was obtained from all subjects involved in the study.","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 that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"569"}}