Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer.

Hossain, MJ; Chowdhury, UN; Islam, MB; Uddin, S; Ahmed, MB; Quinn, JMW; Moni, MA

Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer.

Hossain, MJ Chowdhury, UN Islam, MB Uddin, S Ahmed, MB Quinn, JMW Moni, MA

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Comput Biol Med, 2021, 135, pp. 104539
Issue Date:: 2021-08

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Field	Value	Language
dc.contributor.author	Hossain, MJ
dc.contributor.author	Chowdhury, UN
dc.contributor.author	Islam, MB
dc.contributor.author	Uddin, S
dc.contributor.author	Ahmed, MB
dc.contributor.author	Quinn, JMW
dc.contributor.author	Moni, MA
dc.date.accessioned	2022-05-08T23:03:20Z
dc.date.available	2021-05-26
dc.date.available	2022-05-08T23:03:20Z
dc.date.issued	2021-08
dc.identifier.citation	Comput Biol Med, 2021, 135, pp. 104539
dc.identifier.issn	0010-4825
dc.identifier.issn	1879-0534
dc.identifier.uri	http://hdl.handle.net/10453/157137
dc.description.abstract	Colorectal cancer (CRC) is one of the most common and lethal malignant lesions. Determining how the identified risk factors drive the formation and development of CRC could be an essential means for effective therapeutic development. Aiming this, we investigated how the altered gene expression resulting from exposure to putative CRC risk factors contribute to prognostic biomarker identification. Differentially expressed genes (DEGs) were first identified for CRC and other eight risk factors. Gene set enrichment analysis (GSEA) through the molecular pathway and gene ontology (GO), as well as protein-protein interaction (PPI) network, were then conducted to predict the functions of these DEGs. Our identified genes were explored through the dbGaP and OMIM databases to compare with the already identified and known prognostic CRC biomarkers. The survival time of CRC patients was also examined using a Cox Proportional Hazard regression-based prognostic model by integrating transcriptome data from The Cancer Genome Atlas (TCGA). In this study, PPI analysis identified 4 sub-networks and 8 hub genes that may be potential therapeutic targets, including CXCL8, ICAM1, SOD2, CXCL2, CCL20, OIP5, BUB1, ASPM and IL1RN. We also identified seven signature genes (PRR5.ARHGAP8, CA7, NEDD4L, GFR2, ARHGAP8, SMTN, OIP5) in independent analysis and among which PRR5. ARHGAP8 was found in both multivariate analyses and in analyses that combined gene expression and clinical information. This approach provides both mechanistic information and, when combined with predictive clinical information, good evidence that the identified genes are significant biomarkers of processes involved in CRC progression and survival.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Comput Biol Med
dc.relation.isbasedon	10.1016/j.compbiomed.2021.104539
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences
dc.subject.classification	Biomedical Engineering
dc.subject.mesh	Biomarkers, Tumor
dc.subject.mesh	Colorectal Neoplasms
dc.subject.mesh	Cytoskeletal Proteins
dc.subject.mesh	Databases, Genetic
dc.subject.mesh	GTPase-Activating Proteins
dc.subject.mesh	Gene Expression Regulation, Neoplastic
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Muscle Proteins
dc.subject.mesh	Risk Factors
dc.subject.mesh	Transcriptome
dc.subject.mesh	Humans
dc.subject.mesh	Colorectal Neoplasms
dc.subject.mesh	GTPase-Activating Proteins
dc.subject.mesh	Muscle Proteins
dc.subject.mesh	Cytoskeletal Proteins
dc.subject.mesh	Risk Factors
dc.subject.mesh	Gene Expression Regulation, Neoplastic
dc.subject.mesh	Databases, Genetic
dc.subject.mesh	Transcriptome
dc.subject.mesh	Machine Learning
dc.subject.mesh	Biomarkers, Tumor
dc.title	Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer.
dc.type	Journal Article
utslib.citation.volume	135
utslib.location.activity	United States
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-08T23:03:14Z
pubs.publication-status	Published
pubs.volume	135

Abstract:

Colorectal cancer (CRC) is one of the most common and lethal malignant lesions. Determining how the identified risk factors drive the formation and development of CRC could be an essential means for effective therapeutic development. Aiming this, we investigated how the altered gene expression resulting from exposure to putative CRC risk factors contribute to prognostic biomarker identification. Differentially expressed genes (DEGs) were first identified for CRC and other eight risk factors. Gene set enrichment analysis (GSEA) through the molecular pathway and gene ontology (GO), as well as protein-protein interaction (PPI) network, were then conducted to predict the functions of these DEGs. Our identified genes were explored through the dbGaP and OMIM databases to compare with the already identified and known prognostic CRC biomarkers. The survival time of CRC patients was also examined using a Cox Proportional Hazard regression-based prognostic model by integrating transcriptome data from The Cancer Genome Atlas (TCGA). In this study, PPI analysis identified 4 sub-networks and 8 hub genes that may be potential therapeutic targets, including CXCL8, ICAM1, SOD2, CXCL2, CCL20, OIP5, BUB1, ASPM and IL1RN. We also identified seven signature genes (PRR5.ARHGAP8, CA7, NEDD4L, GFR2, ARHGAP8, SMTN, OIP5) in independent analysis and among which PRR5. ARHGAP8 was found in both multivariate analyses and in analyses that combined gene expression and clinical information. This approach provides both mechanistic information and, when combined with predictive clinical information, good evidence that the identified genes are significant biomarkers of processes involved in CRC progression and survival.

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http://hdl.handle.net/10453/157137