Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer
Because cancer is a leading cause of death and is thought to be caused by genetic errors or genomic instability in many circumstances, there have been studies exploring cancer’s genetic basis using microarray and RNA-seq methods, linking gene expression data to patient survival. This research introd...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/25/6/3317 |
| _version_ | 1797240663177166848 |
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| author | Zehwan Kim Jaebon Lee Ye Eun Yoon Jae Won Yun |
| author_facet | Zehwan Kim Jaebon Lee Ye Eun Yoon Jae Won Yun |
| author_sort | Zehwan Kim |
| collection | DOAJ |
| description | Because cancer is a leading cause of death and is thought to be caused by genetic errors or genomic instability in many circumstances, there have been studies exploring cancer’s genetic basis using microarray and RNA-seq methods, linking gene expression data to patient survival. This research introduces a methodological framework, combining heterogeneous gene expression data, random forest selection, and pathway analysis, alongside clinical information and Cox regression analysis, to discover prognostic biomarkers. Heterogeneous gene expression data for colorectal cancer were collected from TCGA-COAD (RNA-seq), and GSE17536 and GSE39582 (microarray), and were integrated with Entrez Gene IDs. Using Cox regression analysis and random forest, genes with consistent hazard ratios and significantly affecting patient survivability were chosen. Predictive accuracy was evaluated using ROC curves. Pathway analysis identified potential RNA biomarkers. The authors identified 28 RNA biomarkers. Pathway analysis revealed enrichment in cancer-related pathways, notably EGFR downstream signaling and IGF1R signaling. Three RNA biomarkers (ZEB1-AS1, PI4K2A, and ITGB8-AS1) and two clinical biomarkers (stage and age) were chosen for a prognostic model, improving predictive performance compared to using clinical biomarkers alone. Despite biomarker identification challenges, this study underscores integration of heterogenous gene expression data for discovery. |
| first_indexed | 2024-04-24T18:11:00Z |
| format | Article |
| id | doaj.art-197692533cf844f69378e07d18b7f30c |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-04-24T18:11:00Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-197692533cf844f69378e07d18b7f30c2024-03-27T13:45:41ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672024-03-01256331710.3390/ijms25063317Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal CancerZehwan Kim0Jaebon Lee1Ye Eun Yoon2Jae Won Yun3Department of Laboratory Medicine, Yeungnam University College of Medicine, Daegu 42415, Republic of KoreaVeterans Health Service Medical Research Institute, Veterans Health Service Medical Center, Seoul 05368, Republic of KoreaVeterans Health Service Medical Research Institute, Veterans Health Service Medical Center, Seoul 05368, Republic of KoreaVeterans Health Service Medical Research Institute, Veterans Health Service Medical Center, Seoul 05368, Republic of KoreaBecause cancer is a leading cause of death and is thought to be caused by genetic errors or genomic instability in many circumstances, there have been studies exploring cancer’s genetic basis using microarray and RNA-seq methods, linking gene expression data to patient survival. This research introduces a methodological framework, combining heterogeneous gene expression data, random forest selection, and pathway analysis, alongside clinical information and Cox regression analysis, to discover prognostic biomarkers. Heterogeneous gene expression data for colorectal cancer were collected from TCGA-COAD (RNA-seq), and GSE17536 and GSE39582 (microarray), and were integrated with Entrez Gene IDs. Using Cox regression analysis and random forest, genes with consistent hazard ratios and significantly affecting patient survivability were chosen. Predictive accuracy was evaluated using ROC curves. Pathway analysis identified potential RNA biomarkers. The authors identified 28 RNA biomarkers. Pathway analysis revealed enrichment in cancer-related pathways, notably EGFR downstream signaling and IGF1R signaling. Three RNA biomarkers (ZEB1-AS1, PI4K2A, and ITGB8-AS1) and two clinical biomarkers (stage and age) were chosen for a prognostic model, improving predictive performance compared to using clinical biomarkers alone. Despite biomarker identification challenges, this study underscores integration of heterogenous gene expression data for discovery.https://www.mdpi.com/1422-0067/25/6/3317prognostic biomarkerscolorectal cancergene expressionRNA |
| spellingShingle | Zehwan Kim Jaebon Lee Ye Eun Yoon Jae Won Yun Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer International Journal of Molecular Sciences prognostic biomarkers colorectal cancer gene expression RNA |
| title | Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer |
| title_full | Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer |
| title_fullStr | Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer |
| title_full_unstemmed | Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer |
| title_short | Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer |
| title_sort | unveiling prognostic rna biomarkers through a multi cohort study in colorectal cancer |
| topic | prognostic biomarkers colorectal cancer gene expression RNA |
| url | https://www.mdpi.com/1422-0067/25/6/3317 |
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