Update of gene expression/methylation and MiRNA profiling in colorectal cancer; application in diagnosis, prognosis, and targeted therapy
Colorectal cancer is one of the most deadliest malignancies worldwide. Due to the dearth of appropriate biomarkers, the diagnosis of this mortal disease is usually deferred, in its turn, culminating in the failure of prevention. By the same token, proper biomarkers are at play in determining the qua...
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| description | Colorectal cancer is one of the most deadliest malignancies worldwide. Due to the dearth of appropriate biomarkers, the diagnosis of this mortal disease is usually deferred, in its turn, culminating in the failure of prevention. By the same token, proper biomarkers are at play in determining the quality of prognosis. In other words, the survival rate is contingent upon the regulation of such biomarkers.
The information regarding expression (GSE41258, and GSE31905), methylation (GSE101764), and miRNA (dbDEMC) were downloaded. MEXPRESS and GEPIA confirmed the validated differentially expressed/methylated genes using TCGA data. Taking advantage of the correlation plots and receiver-operating-characteristic (ROC) curves, expression and methylation profiles were compared. The interactions between validated differentially expressed genes and differentially expressed miRNA were recognized and visualized by miRTarBase and Cytoscape, respectively. Then, the protein-protein interaction (PPI) network and hub genes were established via STRING and Cytohubba plugin. Utilizing R packages (DOSE, Enrichplot, and clusterProfiler) and DAVID database, the Functional Enrichment analysis and the detection of KEGG pathways were performed. Ultimately, in order to recognize the prognostic value of found biomarkers, they were evaluated through drawing survival plots for CRC patients.
In this research, we found an expression profile (with 13 novel genes), a methylation profile (with two novel genes), and a miRNA profile with diagnostic value. Concerning diagnosis, the expression profile was evaluated more powerful in comparison with the methylation profile. Furthermore, a prognosis-related expression profile was detected.
In addition to diagnostic- and prognostic-applicability, the discerned profiles can assist in targeted therapy and current therapeutic strategies. |
| doi_str_mv | 10.1371/journal.pone.0265527 |
| format | Article |
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The information regarding expression (GSE41258, and GSE31905), methylation (GSE101764), and miRNA (dbDEMC) were downloaded. MEXPRESS and GEPIA confirmed the validated differentially expressed/methylated genes using TCGA data. Taking advantage of the correlation plots and receiver-operating-characteristic (ROC) curves, expression and methylation profiles were compared. The interactions between validated differentially expressed genes and differentially expressed miRNA were recognized and visualized by miRTarBase and Cytoscape, respectively. Then, the protein-protein interaction (PPI) network and hub genes were established via STRING and Cytohubba plugin. Utilizing R packages (DOSE, Enrichplot, and clusterProfiler) and DAVID database, the Functional Enrichment analysis and the detection of KEGG pathways were performed. Ultimately, in order to recognize the prognostic value of found biomarkers, they were evaluated through drawing survival plots for CRC patients.
In this research, we found an expression profile (with 13 novel genes), a methylation profile (with two novel genes), and a miRNA profile with diagnostic value. Concerning diagnosis, the expression profile was evaluated more powerful in comparison with the methylation profile. Furthermore, a prognosis-related expression profile was detected.
In addition to diagnostic- and prognostic-applicability, the discerned profiles can assist in targeted therapy and current therapeutic strategies.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0265527</identifier><identifier>PMID: 35333898</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Biology and Life Sciences ; Biomarkers ; Biomarkers, Tumor - genetics ; Biomarkers, Tumor - metabolism ; Breast cancer ; Cancer ; Care and treatment ; Colorectal cancer ; Colorectal carcinoma ; Colorectal Neoplasms - diagnosis ; Colorectal Neoplasms - genetics ; Colorectal Neoplasms - therapy ; Computational Biology ; Datasets ; Diagnosis ; DNA methylation ; Gastric cancer ; Gene Expression ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Gene set enrichment analysis ; Genes ; Genetic aspects ; Health aspects ; Humans ; Liver cancer ; Medical diagnosis ; Medical prognosis ; Medicine and Health Sciences ; Methylation ; MicroRNA ; MicroRNAs - genetics ; MicroRNAs - metabolism ; miRNA ; Physical Sciences ; Prognosis ; Protein interaction ; Protein Interaction Maps - genetics ; Protein-protein interactions ; Proteins ; Risk factors ; Survival</subject><ispartof>PloS one, 2022-03, Vol.17 (3), p.e0265527-e0265527</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Mehrgou, Teimourian. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Mehrgou, Teimourian 2022 Mehrgou, Teimourian</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c622t-e82f94d9216a441a6d5a914b4ca7c9e096afbc9ceeb973f14d0591e3617db59d3</citedby><cites>FETCH-LOGICAL-c622t-e82f94d9216a441a6d5a914b4ca7c9e096afbc9ceeb973f14d0591e3617db59d3</cites><orcidid>0000-0002-8642-9128</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956198/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956198/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35333898$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Andrés-León, Eduardo</contributor><creatorcontrib>Mehrgou, Amir</creatorcontrib><creatorcontrib>Teimourian, Shahram</creatorcontrib><title>Update of gene expression/methylation and MiRNA profiling in colorectal cancer; application in diagnosis, prognosis, and targeted therapy</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Colorectal cancer is one of the most deadliest malignancies worldwide. Due to the dearth of appropriate biomarkers, the diagnosis of this mortal disease is usually deferred, in its turn, culminating in the failure of prevention. By the same token, proper biomarkers are at play in determining the quality of prognosis. In other words, the survival rate is contingent upon the regulation of such biomarkers.
The information regarding expression (GSE41258, and GSE31905), methylation (GSE101764), and miRNA (dbDEMC) were downloaded. MEXPRESS and GEPIA confirmed the validated differentially expressed/methylated genes using TCGA data. Taking advantage of the correlation plots and receiver-operating-characteristic (ROC) curves, expression and methylation profiles were compared. The interactions between validated differentially expressed genes and differentially expressed miRNA were recognized and visualized by miRTarBase and Cytoscape, respectively. Then, the protein-protein interaction (PPI) network and hub genes were established via STRING and Cytohubba plugin. Utilizing R packages (DOSE, Enrichplot, and clusterProfiler) and DAVID database, the Functional Enrichment analysis and the detection of KEGG pathways were performed. Ultimately, in order to recognize the prognostic value of found biomarkers, they were evaluated through drawing survival plots for CRC patients.
In this research, we found an expression profile (with 13 novel genes), a methylation profile (with two novel genes), and a miRNA profile with diagnostic value. Concerning diagnosis, the expression profile was evaluated more powerful in comparison with the methylation profile. Furthermore, a prognosis-related expression profile was detected.
In addition to diagnostic- and prognostic-applicability, the discerned profiles can assist in targeted therapy and current therapeutic strategies.</description><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Breast cancer</subject><subject>Cancer</subject><subject>Care and treatment</subject><subject>Colorectal cancer</subject><subject>Colorectal carcinoma</subject><subject>Colorectal Neoplasms - diagnosis</subject><subject>Colorectal Neoplasms - genetics</subject><subject>Colorectal Neoplasms - therapy</subject><subject>Computational Biology</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>DNA methylation</subject><subject>Gastric cancer</subject><subject>Gene Expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Gene Regulatory Networks</subject><subject>Gene set enrichment analysis</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Liver cancer</subject><subject>Medical diagnosis</subject><subject>Medical prognosis</subject><subject>Medicine and Health Sciences</subject><subject>Methylation</subject><subject>MicroRNA</subject><subject>MicroRNAs - 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Due to the dearth of appropriate biomarkers, the diagnosis of this mortal disease is usually deferred, in its turn, culminating in the failure of prevention. By the same token, proper biomarkers are at play in determining the quality of prognosis. In other words, the survival rate is contingent upon the regulation of such biomarkers.
The information regarding expression (GSE41258, and GSE31905), methylation (GSE101764), and miRNA (dbDEMC) were downloaded. MEXPRESS and GEPIA confirmed the validated differentially expressed/methylated genes using TCGA data. Taking advantage of the correlation plots and receiver-operating-characteristic (ROC) curves, expression and methylation profiles were compared. The interactions between validated differentially expressed genes and differentially expressed miRNA were recognized and visualized by miRTarBase and Cytoscape, respectively. Then, the protein-protein interaction (PPI) network and hub genes were established via STRING and Cytohubba plugin. Utilizing R packages (DOSE, Enrichplot, and clusterProfiler) and DAVID database, the Functional Enrichment analysis and the detection of KEGG pathways were performed. Ultimately, in order to recognize the prognostic value of found biomarkers, they were evaluated through drawing survival plots for CRC patients.
In this research, we found an expression profile (with 13 novel genes), a methylation profile (with two novel genes), and a miRNA profile with diagnostic value. Concerning diagnosis, the expression profile was evaluated more powerful in comparison with the methylation profile. Furthermore, a prognosis-related expression profile was detected.
In addition to diagnostic- and prognostic-applicability, the discerned profiles can assist in targeted therapy and current therapeutic strategies.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35333898</pmid><doi>10.1371/journal.pone.0265527</doi><tpages>e0265527</tpages><orcidid>https://orcid.org/0000-0002-8642-9128</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Biology and Life Sciences Biomarkers Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Breast cancer Cancer Care and treatment Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - diagnosis Colorectal Neoplasms - genetics Colorectal Neoplasms - therapy Computational Biology Datasets Diagnosis DNA methylation Gastric cancer Gene Expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Gene set enrichment analysis Genes Genetic aspects Health aspects Humans Liver cancer Medical diagnosis Medical prognosis Medicine and Health Sciences Methylation MicroRNA MicroRNAs - genetics MicroRNAs - metabolism miRNA Physical Sciences Prognosis Protein interaction Protein Interaction Maps - genetics Protein-protein interactions Proteins Risk factors Survival |
| title | Update of gene expression/methylation and MiRNA profiling in colorectal cancer; application in diagnosis, prognosis, and targeted therapy |
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