Omics Integration Analysis Unravel the Landscape of Driving Mechanisms of Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33369450", "kind": "science-record", "revision": 8, "content_hash": "sha256:4bc9b972d22513e18098ca13e0a81b5320d5e7c855e76d86d67e62ef25af70a3", "created_at": "2026-11-01T15:41:20Z", "updated_at": "2026-11-01T22:45:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Omics Integration Analysis Unravel the Landscape of Driving Mechanisms of Colorectal Cancer.", "doi": "10.31557/APJCP.2020.21.12.3539", "pmid": "33369450", "authors": ["Fatemeh Nikmanesh", "Shamim Sarhadi", "Mehdi Dadashpour", "Yazdan Asghari", "Nosratollah Zarghami"], "journal": "Asian Pacific journal of cancer prevention : APJCP", "year": 2020, "abstract": "Colorectal cancer (CRC) is one of the most malignant cancers and results in a substantial rate of morbidity and mortality. Diagnosis of this malignancy in early stages increases the chance of effective treatment. High-throughput data analyses reveal omics signatures and also provide the possibility of developing computational models for early detection of this disease. Such models would be able to use as complementary tools for early detection of different types of cancers including CRC. In this study, using gene expression data, the Flux balance analysis (FBA) applied to decode metabolic fluxes in cancer and normal cells. Moreover, transcriptome and genome analyses revealed driver agents of CRC in a biological network scheme. By applying comprehensive publicly available data from TCGA, different aspect of CRC regulome including the regulatory effect of gene expression, methylation, microRNA, copy number aberration and point mutation profile over protein levels investigated and the results provide a regulatory picture underlying CRC. Compiling omics profiles indicated snapshots of changes in different omics levels and flux rate of CRC. In conclusion, considering obtained CRC signatures and their role in biological operating systems of cells, the results suggest reliable driver regulatory modules that could potentially serve as biomarkers and therapeutic targets and furthermore expand our understanding of driving mechanisms of this disease.<br />.", "claims": [{"id": "claim-method", "statement": "To find differentially expressed genes (DEGs) in CRC cells in comparison with the normal cells, gene expression microarray data sets preprocessed by FRMA package (McCall et al., 2010) in R and then limma (Smyth, 2005) was used to develop a linear model for finding DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33369450", "locator": "sections[2] \"Materials and Methods\" paragraph 13", "quote": "To find differentially expressed genes (DEGs) in CRC cells in comparison with the normal cells, gene expression microarray data sets preprocessed by FRMA package (McCall et al., 2010) in R and then limma (Smyth, 2005) was used to develop a linear model for finding DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Gene expression microarray data related to colorectal cancer were downloaded from the Gene Expression Omnibus (GEO) database. 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