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Targeting colorectal cancer liver metastasis through repurposing metabolic and immune inhibitors: A theoretical study.

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Computational biology and chemistry 📖 저널 OA 8.1% 2024: 1/4 OA 2025: 0/12 OA 2026: 6/70 OA 2024~2026 2026 Vol.120(Pt 2) p. 108689 OA
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유사 논문
P · Population 대상 환자/모집단
환자: advanced colorectal cancer (CRC) and remains a difficult challenge in oncology
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Additionally, we predict possible improvements for CRLM treatment using an immunotherapy drug like Pembrolizumab. Overall, this paper suggests potential combinations requiring experimental validation for drug repurposing to improve CRLM treatment outcomes.

Mondal M, Lahiri A, Datta A

📝 환자 설명용 한 줄

Liver metastasis from colorectal cancer is a major cause of death in patients with advanced colorectal cancer (CRC) and remains a difficult challenge in oncology.

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APA Mondal M, Lahiri A, Datta A (2026). Targeting colorectal cancer liver metastasis through repurposing metabolic and immune inhibitors: A theoretical study.. Computational biology and chemistry, 120(Pt 2), 108689. https://doi.org/10.1016/j.compbiolchem.2025.108689
MLA Mondal M, et al.. "Targeting colorectal cancer liver metastasis through repurposing metabolic and immune inhibitors: A theoretical study.." Computational biology and chemistry, vol. 120, no. Pt 2, 2026, pp. 108689.
PMID 41086644 ↗

Abstract

Liver metastasis from colorectal cancer is a major cause of death in patients with advanced colorectal cancer (CRC) and remains a difficult challenge in oncology. In spite of commendable developments in medical inventions, the complexity and poor prognosis of metastatic stage, alliterative strategies are required to address Colorectal cancer liver metastasis (CRLM). This paper presents a computational study on drug repurposing for CRLM using a Boolean Network model. We comprehensively analyze CRLM signaling pathways such as WNT, PI3K/AKT/mTOR, MAPK, Hedgehog, TGF-β, NF-kB, NOTCH and HGF and target them with metabolic and immune inhibitors. This study utilizes a computational analysis to evaluate single and multi-agent treatments across scenarios involving single and multiple genetic mutations. Our findings highlights the efficacy of metabolic inhibitors such as Simvastatin, Metformin, and predict compatible partner drugs to enhance their efficacy. Additionally, we predict possible improvements for CRLM treatment using an immunotherapy drug like Pembrolizumab. Overall, this paper suggests potential combinations requiring experimental validation for drug repurposing to improve CRLM treatment outcomes.

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