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Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease
Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we appli...
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Published in: | Biomolecules (Basel, Switzerland) Switzerland), 2024-01, Vol.14 (2), p.164 |
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creator | Lucena-Padros, Helena Bravo-Gil, Nereida Tous, Cristina Rojano, Elena Seoane-Zonjic, Pedro Fernández, Raquel María Ranea, Juan A G Antiñolo, Guillermo Borrego, Salud |
description | Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with
as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations. |
doi_str_mv | 10.3390/biom14020164 |
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as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.</description><subject>Analysis</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Causes and theories of causation</subject><subject>Colon</subject><subject>Computational Biology</subject><subject>Disease</subject><subject>Diseases</subject><subject>enteric neuropathy</subject><subject>Ganglia</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Hirschsprung Disease - genetics</subject><subject>Hirschsprung's disease</subject><subject>Humans</subject><subject>MicroRNAs</subject><subject>MicroRNAs - genetics</subject><subject>miRNA</subject><subject>Multiomics</subject><subject>networks analysis</subject><subject>omics expression data</subject><subject>Online data bases</subject><subject>Physiological aspects</subject><subject>Proteins</subject><subject>system biology</subject><issn>2218-273X</issn><issn>2218-273X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkstv1DAQxiMEolXbG2cUiQsH0vqVxD5uX3Sl0nIAiZs1fmTxksRb2wH639fpltIibEu2Rr_vG3s8RfEGo0NKBTpSzg-YIYJww14Uu4RgXpGWfnv55LxTHMS4RnnwvAh9XexQTkXLEN4t0rHzbux8GCA5HcvPwRqnk_NjmYPllU2_fPhRHUO0plyOya5CBn_a8tPUJ1ddD7Po7Pcm2Bhn0SkkKBcj9LfRxdKN5YULUX-PmzCNq_LURZud9otXHfTRHjzse8XX87MvJxfV5fXH5cnistJMoFQBqhVRtBa2ZqI2jBjVcctAcaoVBg207Zg2mGLWQsNU09JW1QAt4p1RBtG9Yrn1NR7WchPcAOFWenDyPuDDSkLIz-6t1LkehLNOaWRYwzFHNREmV4sYbojS2ev91msT_M1kY5KDi9r2PYzWT1ESQQkiRKA57bt_0LWfQq7JPYVqVGMm_lIryPnnP0gB9GwqFy1niDaCkEwd_ofK09hcej_azuX4M8GHrUAHH2Ow3eO7MZJzz8inPZPxtw93ndRgzSP8p0PoHVbPu3k</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Lucena-Padros, Helena</creator><creator>Bravo-Gil, Nereida</creator><creator>Tous, Cristina</creator><creator>Rojano, Elena</creator><creator>Seoane-Zonjic, Pedro</creator><creator>Fernández, Raquel María</creator><creator>Ranea, Juan A G</creator><creator>Antiñolo, Guillermo</creator><creator>Borrego, Salud</creator><general>MDPI AG</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7T5</scope><scope>7TM</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-3020-1415</orcidid><orcidid>https://orcid.org/0000-0002-2678-710X</orcidid></search><sort><creationdate>20240101</creationdate><title>Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease</title><author>Lucena-Padros, Helena ; 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HSCR has a complex inheritance, with
as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>38397401</pmid><doi>10.3390/biom14020164</doi><orcidid>https://orcid.org/0000-0002-3020-1415</orcidid><orcidid>https://orcid.org/0000-0002-2678-710X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Bioinformatics Biomarkers Causes and theories of causation Colon Computational Biology Disease Diseases enteric neuropathy Ganglia Genes Genetic aspects Hirschsprung Disease - genetics Hirschsprung's disease Humans MicroRNAs MicroRNAs - genetics miRNA Multiomics networks analysis omics expression data Online data bases Physiological aspects Proteins system biology |
title | Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease |
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