Genetics and Comorbidity network analysis of Colorectal cancer

Genetics and Comorbidity network analysis of Colorectal cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Genetics and Comorbidity network analysis of Colorectal cancer Branimir Ljubic, Martin Pavlovski, Jumanah Alshehri, Vladimir Bajic, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.2.13187/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jan, 2020 Read the published version in Informatics in Medicine Unlocked → Version 1 posted You are reading this latest preprint version Abstract Background Colorectal cancer (CRC) is the third most common cancer in the United States, and the second leading cause of cancer death. Comorbidity network analyses of CRC can help understanding of the illness progression. About 10%-30% of patients have a family history of CRC that suggests a hereditary contribution, including pathogenic variants of genes. The goal was to identify comorbidities associated with CRC and to discover pathogenic variants of genes that could be connected to CRC and comorbidity diseases. Results A novel model is developed based on comorbidity networks, to study progression of CRC. The model was developed on the HCUP, SID California inpatient database for a period of 9 years (2003-2011). Comorbidity networks, and Venn diagrams show probabilities of occurrence of comorbidities associated with different stages of CRC. Ranked lists of comorbidities (more than 5,800) were utilized for text mining of PubMed and expert curated sources (DisGeNet), to identify genes associated with CRC. Associations between 1,940 different genes and CRC were extracted from PubMed. 150 different genes are associated with CRC in DisGeNet. 96 genes are present in both sources (PubMed and curated sources). The most mentioned gene associated with CRC were: TP53 (241 abstracts in PubMed), APC (115), and KRAS (106). All 3 genes had DisGeNet scores of 0.5. Two more genes (MLH1 – 98 abstracts and TGFBR2 - 18 abstracts) had the same DisGeNet scores. PPARG gene (43 abstracts) had DisGeNet score of 0.6. Genes described on the cancer.gov website were found on PubMed too. MUTYH was described in association with CRC in 7 abstracts, MSH6 in 25 abstracts, PMS2 in 11, EPCAM in 10, POLD1 in 4, BMPR1A in 3, SMAD4 in 15, PTEN in 25 and STK11 in 8 PubMed abstracts. Lists of associations of the six most common genes and comorbidities of CRC were also created. Conclusions Results of the study could be considered as a novelty contributing to better understanding of risk factors (genes, comorbidities) for the development of CRC. Genetic findings could lead to the improvement of guidelines for genetic testing of patients that are at increased risk to develop CRC. Cancer Biology Oncology Colorectal cancer Comorbidities Genes Treatment procedures Social networks Biomedical informatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Cite Share Download PDF Status: Published Journal Publication published 01 Jan, 2020 Read the published version in Informatics in Medicine Unlocked → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3901","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":134596,"identity":"74e1ff53-43a7-4e16-a1b1-41fe1c9b07dc","order_by":1,"name":"Branimir 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target='_blank'\u003edownload as a PDF\u003c/a\u003e.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Comorbidities, Genes, Treatment procedures, Social networks, Biomedical informatics","lastPublishedDoi":"10.21203/rs.2.13187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.2.13187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background Colorectal cancer (CRC) is the third most common cancer in the United States, and the second leading cause of cancer death. Comorbidity network analyses of CRC can help understanding of the illness progression. About 10%-30% of patients have a family history of CRC that suggests a hereditary contribution, including pathogenic variants of genes. The goal was to identify comorbidities associated with CRC and to discover pathogenic variants of genes that could be connected to CRC and comorbidity diseases. Results A novel model is developed based on comorbidity networks, to study progression of CRC. The model was developed on the HCUP, SID California inpatient database for a period of 9 years (2003-2011). Comorbidity networks, and Venn diagrams show probabilities of occurrence of comorbidities associated with different stages of CRC. Ranked lists of comorbidities (more than 5,800) were utilized for text mining of PubMed and expert curated sources (DisGeNet), to identify genes associated with CRC. Associations between 1,940 different genes and CRC were extracted from PubMed. 150 different genes are associated with CRC in DisGeNet. 96 genes are present in both sources (PubMed and curated sources). The most mentioned gene associated with CRC were: TP53 (241 abstracts in PubMed), APC (115), and KRAS (106). All 3 genes had DisGeNet scores of 0.5. Two more genes (MLH1 – 98 abstracts and TGFBR2 - 18 abstracts) had the same DisGeNet scores. PPARG gene (43 abstracts) had DisGeNet score of 0.6. Genes described on the cancer.gov website were found on PubMed too. MUTYH was described in association with CRC in 7 abstracts, MSH6 in 25 abstracts, PMS2 in 11, EPCAM in 10, POLD1 in 4, BMPR1A in 3, SMAD4 in 15, PTEN in 25 and STK11 in 8 PubMed abstracts. Lists of associations of the six most common genes and comorbidities of CRC were also created. Conclusions Results of the study could be considered as a novelty contributing to better understanding of risk factors (genes, comorbidities) for the development of CRC. Genetic findings could lead to the improvement of guidelines for genetic testing of patients that are at increased risk to develop CRC.","manuscriptTitle":"Genetics and Comorbidity network analysis of Colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2019-08-30 01:45:51","doi":"10.21203/rs.2.13187/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f211611-059d-4d82-aa47-43994705a27f","owner":[],"postedDate":"August 30th, 2019","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":21998,"name":"Cancer Biology"},{"id":21999,"name":"Oncology"}],"tags":[],"updatedAt":"2021-07-22T18:54:55+00:00","versionOfRecord":{"articleIdentity":"rs-3901","link":"https://doi.org/10.1016/j.imu.2020.100492","journal":{"identity":"informatics-in-medicine-unlocked","isVorOnly":true,"title":"Informatics in Medicine Unlocked"},"publishedOn":"2020-01-01 18:54:55","publishedOnDateReadable":"January 1st, 2020"},"versionCreatedAt":"2019-08-30 01:45:51","video":"","vorDoi":"10.1016/j.imu.2020.100492","vorDoiUrl":"https://doi.org/10.1016/j.imu.2020.100492","workflowStages":[]},"version":"v1","identity":"rs-3901","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-3901","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}