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Copyright (c) 2025 Naif Hassan Almazrou, Emad Alawy Sharahily, Amal Abdullah Ali Alomran, Samer Olaythah Ahmed Alsubhi, Samy Fhaid Sulaiman Alsamy, Atiaf Fatehaldein Ali Yousef, Fatemah Hassan Mobarki, Mansour Ahmed Salman Amri, Manal Abdullah Ali Aldosari, Zainab Abdullah Saleh Albishi, Meaad Ali Mohammed Buayti

This work is licensed under a Creative Commons Attribution 4.0 International License.
Interdisciplinary Diagnosis and Management of Colorectal Cancer for Nursing, Radiology, Laboratory, Nutrition, and Health Informatics Professionals
Corresponding Author(s) : Naif Hassan Almazrou
Saudi Journal of Medicine and Public Health, Vol. 2 No. 2 (2025)
Abstract
Colorectal cancer (CRC) remains a significant global health burden, ranking as the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide. The complex molecular landscape, heterogeneous presentation, and multifaceted treatment approaches necessitate a collaborative, interdisciplinary framework for optimal patient outcomes. This comprehensive review synthesizes current evidence on CRC pathophysiology, risk factors, diagnostic modalities, and therapeutic strategies while emphasizing the critical roles of nursing, radiology, laboratory medicine, and health informatics professionals in the multidisciplinary care team. Recent advances in genomics, epigenetics, metabolic reprogramming, and artificial intelligence have revolutionized CRC diagnosis and management, creating both opportunities and challenges for healthcare professionals across specialties. The integration of molecular biomarkers, advanced imaging techniques, patient-derived organoids, and computational approaches has enabled more precise risk stratification, early detection, and personalized treatment selection. However, disparities in screening participation, healthcare access, and implementation of novel technologies persist. This review provides a framework for interdisciplinary collaboration, highlighting how nursing professionals can optimize patient education and supportive care, radiology experts can enhance diagnostic accuracy through advanced imaging and radiomics, laboratory specialists can facilitate molecular characterization and biomarker-guided therapy, and health informatics professionals can leverage artificial intelligence and data integration to improve clinical decision-making. By fostering effective communication and coordinated care across disciplines, healthcare systems can address the multifaceted challenges of CRC management and improve patient outcomes.
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- [3] Matsuda T, Fujimoto A, Igarashi Y. Colorectal Cancer: Epidemiology, Risk Factors, and Public Health Strategies. 2025;106:91-99.
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- [7] Guinney J, Dienstmann R, Wang X, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21(11):1350-1356.
- [8] Valdeolivas A, Amberg B, Giroud N, et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. NPJ Precis Oncol. 2024;8:10.
- [9] Andre T, Shiu KK, Kim TW, et al. Pembrolizumab in Microsatellite-Instability-High Advanced Colorectal Cancer. N Engl J Med. 2020;383:2207-2218.
- [10] Kopetz S, Grothey A, Yaeger R, et al. Encorafenib, Binimetinib, and Cetuximab in BRAF V600E-Mutated Colorectal Cancer. N Engl J Med. 2019;381:1632-1643.
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- [35] Cha PH, Hwang JH, Kwak DK, et al. APC loss induces Warburg effect via increased PKM2 transcription in colorectal cancer. Br J Cancer. 2021;124(3):634-644.
- [36] Zaytseva YY. Lipid Metabolism as a Targetable Metabolic Vulnerability in Colorectal Cancer. Cancers (Basel). 2021;13(2):301.
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- [38] Quan J, Cheng C, Tan Y, et al. Acyl-CoA synthetase long-chain 3-mediated fatty acid oxidation is required for TGFβ1-induced epithelial-mesenchymal transition and metastasis of colorectal carcinoma. Int J Biol Sci. 2022;18(6):2484-2496.
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- [41] Zhao Y, Feng X, Chen Y, et al. 5-Fluorouracil Enhances the Antitumor Activity of the Glutaminase Inhibitor CB-839 against PIK3CA-Mutant Colorectal Cancers. Cancer Res. 2020;80:4815-4827.
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- [55] Durinikova E, Buzo K, Arena S. Preclinical models as patients' avatars for precision medicine in colorectal cancer. J Exp Clin Cancer Res. 2021;40:185.
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- [57] Sirinukunwattana K, Domingo E, Richman SD, et al. Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut. 2021;70:544-554.
- [58] Zamanitajeddin N, Jahanifar M, Bilal M, et al. Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer. Med Image Anal. 2024;93:103071.
- [59] Vayrynen JP, Lau MC, Haruki K, et al. Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer. Clin Cancer Res. 2020;26:4326-4338.
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- [62] Prelaj A, Miskovic V, Zanitti M, et al. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol. 2024;35:29-65.
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[34] Jiang M, Liu S, Lin J, et al. A pan-cancer analysis of molecular characteristics and oncogenic role of hexokinase family genes in human tumors. Life Sci. 2021;264:118669.
[35] Cha PH, Hwang JH, Kwak DK, et al. APC loss induces Warburg effect via increased PKM2 transcription in colorectal cancer. Br J Cancer. 2021;124(3):634-644.
[36] Zaytseva YY. Lipid Metabolism as a Targetable Metabolic Vulnerability in Colorectal Cancer. Cancers (Basel). 2021;13(2):301.
[37] Wen J, Min X, Shen M, et al. ACLY facilitates colon cancer cell metastasis by CTNNB1. J Exp Clin Cancer Res. 2019;38:401.
[38] Quan J, Cheng C, Tan Y, et al. Acyl-CoA synthetase long-chain 3-mediated fatty acid oxidation is required for TGFβ1-induced epithelial-mesenchymal transition and metastasis of colorectal carcinoma. Int J Biol Sci. 2022;18(6):2484-2496.
[39] Tran TQ, Hanse EA, Habowski AN, et al. α-Ketoglutarate attenuates Wnt signaling and drives differentiation in colorectal cancer. Nat Cancer. 2020;1(3):345-358.
[40] Wong CC, Xu J, Bian X, et al. In Colorectal Cancer Cells With Mutant KRAS, SLC25A22-Mediated Glutaminolysis Reduces DNA Demethylation to Increase WNT Signaling. Gastroenterology. 2020;159(6):2163-2180.e6.
[41] Zhao Y, Feng X, Chen Y, et al. 5-Fluorouracil Enhances the Antitumor Activity of the Glutaminase Inhibitor CB-839 against PIK3CA-Mutant Colorectal Cancers. Cancer Res. 2020;80:4815-4827.
[42] Martelli V, Pastorino A, Sobrero AF. Prognostic and predictive molecular biomarkers in advanced colorectal cancer. Pharmacol Ther. 2022;236:108239.
[43] Deming DA. Development of KRAS Inhibitors and Their Role for Metastatic Colorectal Cancer. J Natl Compr Canc Netw. 2025;23:e247067.
[44] Kolisnik T, Sulit AK, Schmeier S, et al. Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models. BMC Cancer. 2023;23:647.
[45] Asghari-Jafarabadi M, Wilkins S, Plazzer JP, et al. Prognostic factors and survival disparities in right-sided versus left-sided colon cancer. Sci Rep. 2024;14:12306.
[46] Kim N, Lee ES, Won SE, et al. Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy. Korean J Radiol. 2022;23:1089-1101.
[47] Foersch S, Glasner C, Woerl AC, et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat Med. 2023;29:430-439.
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[49] Fan A, Wang B, Wang X, et al. Immunotherapy in colorectal cancer: current achievements and future perspective. Int J Biol Sci. 2021;17:3837-3849.
[50] Yazdani A, Yazdani A, Mendez-Giraldez R, et al. Gene expression biomarkers differentiate overall survival of colorectal cancer upon targeted therapies. Res Sq. 2024.
[51] Patel SG, Karlitz JJ, Yen T, et al. The rising tide of early-onset colorectal cancer: a comprehensive review. Lancet Gastroenterol Hepatol. 2022;7:262-274.
[52] Lafarge MW, Domingo E, Sirinukunwattana K, et al. Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy. NPJ Precis Oncol. 2024;8:89.
[53] Martins D, Rodrigues J, Redondo P, et al. Evaluating the Optimal Sequence of Treatment with EGFR Inhibitors and Bevacizumab in RAS Wild-Type Metastatic Colorectal Cancer. Cureus. 2022;14:e23543.
[54] Yi K, Park SH, Kim DU, et al. Patient-derived Organoid Model for Predicting the Chemoresponse in Patients With Colorectal Cancer. In Vivo. 2023;37:1751-1759.
[55] Durinikova E, Buzo K, Arena S. Preclinical models as patients' avatars for precision medicine in colorectal cancer. J Exp Clin Cancer Res. 2021;40:185.
[56] Ruusuvuori P, Valkonen M, Latonen L. Deep learning transforms colorectal cancer biomarker prediction from histopathology images. Cancer Cell. 2023;41:1543-1545.
[57] Sirinukunwattana K, Domingo E, Richman SD, et al. Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut. 2021;70:544-554.
[58] Zamanitajeddin N, Jahanifar M, Bilal M, et al. Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer. Med Image Anal. 2024;93:103071.
[59] Vayrynen JP, Lau MC, Haruki K, et al. Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer. Clin Cancer Res. 2020;26:4326-4338.
[60] Das K, Paltani M, Tripathi PK, et al. Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer. Expert Antitumor Ther. 2023;4:1286-1300.
[61] Schulz S, Jesinghaus M, Foersch S. Multistain deep learning as a prognostic and predictive biomarker in colorectal cancer. Pathologie. 2023;44:104-108.
[62] Prelaj A, Miskovic V, Zanitti M, et al. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol. 2024;35:29-65.
[63] Ding X, Huang H, Fang Z, Jiang J. From Subtypes to Solutions: Integrating CMS Classification with Precision Therapeutics in Colorectal Cancer. Curr Treat Options Oncol. 2024;25:1580-1593.
[64] Dougherty MK, Brenner AT, Crockett SD, et al. Evaluation of interventions intended to increase colorectal cancer screening rates in the United States: a systematic review and meta-analysis. JAMA Intern Med. 2018;178(12):1645-58.
[65] Mishra I, Gupta K, Mishra R, et al. An Exploration of Organoid Technology: Present Advancements, Applications, and Obstacles. Curr Pharm Biotechnol. 2024;25:1000-1020.