Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology
<h3>Background</h3><p dir="ltr">Colorectal adenocarcinoma (COAD) is among the most common causes of cancer-related death globally. Early detection and targeted therapy depend on identifying key molecular biomarkers that drive tumor progression. The molecular heterogeneity...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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| _version_ | 1864513533597712384 |
|---|---|
| author | Rawdhah M. Saleh (21436001) |
| author2 | Reham Mansour (22504025) Heba A. Almaghrbi (21436007) Udhaya Kumar. S (22504028) Anju Surendranath (21436010) Ala-Eddin Al Moustafa (14153205) Alsamman M. Alsamman (8372094) Hatem Zayed (835448) |
| author2_role | author author author author author author author |
| author_facet | Rawdhah M. Saleh (21436001) Reham Mansour (22504025) Heba A. Almaghrbi (21436007) Udhaya Kumar. S (22504028) Anju Surendranath (21436010) Ala-Eddin Al Moustafa (14153205) Alsamman M. Alsamman (8372094) Hatem Zayed (835448) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rawdhah M. Saleh (21436001) Reham Mansour (22504025) Heba A. Almaghrbi (21436007) Udhaya Kumar. S (22504028) Anju Surendranath (21436010) Ala-Eddin Al Moustafa (14153205) Alsamman M. Alsamman (8372094) Hatem Zayed (835448) |
| dc.date.none.fl_str_mv | 2025-07-03T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.gene.2025.149594 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Transcriptomic_profiling_and_bioinformatics-driven_statistical_prioritization_of_CRC_biomarkers_A_step_toward_precision_oncology/30455786 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Genetics Biomedical and clinical sciences Oncology and carcinogenesis Colorectal cancer Transcriptomic biomarkers Integrative bioinformatics Molecular signature Extracellular matrix remodelling Epithelial-mesenchymal transition Survival analysis Computational prioritization Precision oncology |
| dc.title.none.fl_str_mv | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Colorectal adenocarcinoma (COAD) is among the most common causes of cancer-related death globally. Early detection and targeted therapy depend on identifying key molecular biomarkers that drive tumor progression. The molecular heterogeneity of COAD demands robust computational strategies to improve the accuracy of biomarker discovery. </p><h3>Methods</h3><p dir="ltr">We developed and implemented a comprehensive, multi-step bioinformatics and statistical pipeline to systematically prioritize clinically relevant biomarkers in COAD. This pipeline integrated differential gene expression analysis, protein–protein interaction (PPI) network construction, and functional enrichment analysis to identify key hub genes associated with tumor progression. We subsequently applied principal component analysis (PCA) and overall survival modeling to evaluate the diagnostic and prognostic relevance of these candidates. Receiver operating characteristic (ROC) curve analysis was used to assess their sensitivity and specificity. Finally, experimental validation of the prioritized hub genes was conducted via qPCR across three CRC cell lines (LoVo, HCT-116, and HT-29), confirming their upregulation and supporting their clinical potential. </p><h3>Results</h3><p dir="ltr">Our integrative pipeline prioritized five key hub genes (<i>CDH3, CXCL1, MMP1, MMP3, </i>and <i>TGFBI</i>) as significantly upregulated in COAD tissues compared to normal controls. Functional enrichment analysis linked these genes to extracellular matrix degradation, epithelial-mesenchymal transition (EMT), inflammatory signaling, and tumor invasion, underscoring their roles in key oncogenic processes. Survival analysis revealed varying degrees of association with patient prognosis, most notably for CXCL1. Diagnostic performance, assessed by ROC analysis, yielded moderate AUC values (0.669–0.692), supporting their potential as biomarkers. Finally, qPCR validation across three CRC cell lines confirmed robust upregulation of all five genes, reinforcing their biological relevance in COAD progression. </p><h3>Conclusion</h3><p dir="ltr">Our study establishes a reproducible, integrative bioinformatics and statistical framework for the systematic identification of clinically actionable biomarkers in CRC. The five hub genes prioritized (<i>CDH3, CXCL1, MMP1, MMP3, </i>and<i> TGFBI</i>) demonstrated consistent diagnostic and prognostic value, offering a solid basis for the development of non-invasive molecular diagnostics and contributing to precision oncology.</p><h2>Other Information</h2><p dir="ltr">Published in: Gene<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.gene.2025.149594" target="_blank">https://dx.doi.org/10.1016/j.gene.2025.149594</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_57d41c9a2c3e90ae4eea442265e23154 |
| identifier_str_mv | 10.1016/j.gene.2025.149594 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30455786 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncologyRawdhah M. Saleh (21436001)Reham Mansour (22504025)Heba A. Almaghrbi (21436007)Udhaya Kumar. S (22504028)Anju Surendranath (21436010)Ala-Eddin Al Moustafa (14153205)Alsamman M. Alsamman (8372094)Hatem Zayed (835448)Biological sciencesGeneticsBiomedical and clinical sciencesOncology and carcinogenesisColorectal cancerTranscriptomic biomarkersIntegrative bioinformaticsMolecular signatureExtracellular matrix remodellingEpithelial-mesenchymal transitionSurvival analysisComputational prioritizationPrecision oncology<h3>Background</h3><p dir="ltr">Colorectal adenocarcinoma (COAD) is among the most common causes of cancer-related death globally. Early detection and targeted therapy depend on identifying key molecular biomarkers that drive tumor progression. The molecular heterogeneity of COAD demands robust computational strategies to improve the accuracy of biomarker discovery. </p><h3>Methods</h3><p dir="ltr">We developed and implemented a comprehensive, multi-step bioinformatics and statistical pipeline to systematically prioritize clinically relevant biomarkers in COAD. This pipeline integrated differential gene expression analysis, protein–protein interaction (PPI) network construction, and functional enrichment analysis to identify key hub genes associated with tumor progression. We subsequently applied principal component analysis (PCA) and overall survival modeling to evaluate the diagnostic and prognostic relevance of these candidates. Receiver operating characteristic (ROC) curve analysis was used to assess their sensitivity and specificity. Finally, experimental validation of the prioritized hub genes was conducted via qPCR across three CRC cell lines (LoVo, HCT-116, and HT-29), confirming their upregulation and supporting their clinical potential. </p><h3>Results</h3><p dir="ltr">Our integrative pipeline prioritized five key hub genes (<i>CDH3, CXCL1, MMP1, MMP3, </i>and <i>TGFBI</i>) as significantly upregulated in COAD tissues compared to normal controls. Functional enrichment analysis linked these genes to extracellular matrix degradation, epithelial-mesenchymal transition (EMT), inflammatory signaling, and tumor invasion, underscoring their roles in key oncogenic processes. Survival analysis revealed varying degrees of association with patient prognosis, most notably for CXCL1. Diagnostic performance, assessed by ROC analysis, yielded moderate AUC values (0.669–0.692), supporting their potential as biomarkers. Finally, qPCR validation across three CRC cell lines confirmed robust upregulation of all five genes, reinforcing their biological relevance in COAD progression. </p><h3>Conclusion</h3><p dir="ltr">Our study establishes a reproducible, integrative bioinformatics and statistical framework for the systematic identification of clinically actionable biomarkers in CRC. The five hub genes prioritized (<i>CDH3, CXCL1, MMP1, MMP3, </i>and<i> TGFBI</i>) demonstrated consistent diagnostic and prognostic value, offering a solid basis for the development of non-invasive molecular diagnostics and contributing to precision oncology.</p><h2>Other Information</h2><p dir="ltr">Published in: Gene<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.gene.2025.149594" target="_blank">https://dx.doi.org/10.1016/j.gene.2025.149594</a></p>2025-07-03T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.gene.2025.149594https://figshare.com/articles/journal_contribution/Transcriptomic_profiling_and_bioinformatics-driven_statistical_prioritization_of_CRC_biomarkers_A_step_toward_precision_oncology/30455786CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304557862025-07-03T06:00:00Z |
| spellingShingle | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology Rawdhah M. Saleh (21436001) Biological sciences Genetics Biomedical and clinical sciences Oncology and carcinogenesis Colorectal cancer Transcriptomic biomarkers Integrative bioinformatics Molecular signature Extracellular matrix remodelling Epithelial-mesenchymal transition Survival analysis Computational prioritization Precision oncology |
| status_str | publishedVersion |
| title | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology |
| title_full | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology |
| title_fullStr | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology |
| title_full_unstemmed | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology |
| title_short | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology |
| title_sort | Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology |
| topic | Biological sciences Genetics Biomedical and clinical sciences Oncology and carcinogenesis Colorectal cancer Transcriptomic biomarkers Integrative bioinformatics Molecular signature Extracellular matrix remodelling Epithelial-mesenchymal transition Survival analysis Computational prioritization Precision oncology |