Revealing the mechanism of chanling Gao in preventing and treating liver metastasis in colorectal cancer through integrated multi-omics and artificial intelligence

Background: Liver metastasis (LM) is a leading cause of mortality in colorectal cancer (CRC), and currently, no effective therapeutic agents are available. Chanling Gao (CLG) has exhibited inhibitory effects on colorectal cancer liver metastasis (CRLM); however, its exact mechanisms of action remain unclear. The integration of artificial intelligence (AI) with precision traditional Chinese medicine offers a promising approach to enhance therapeutic strategies for CRLM. Objective: This study aims to establish a “diagnosis-prognosis” model for CRC and CRLM utilizing AI, and to explore the potential mechanisms of CLG treatment for CRC and CRLM through biological information analysis and cellular experiments. Study design/methods: A “component-target” network was constructed for elucidating the mechanisms underpinning the therapeutic potential of CLG in CRLM through network pharmacology. Prognostic models for CRC were developed by combining Non-Negative Matrix Factorization (NMF) clustering with ten machine learning (ML) methods, using core targets identified from the network, and validated across multiple TCGA and GEO cohorts. Clinical pathological factors, survival data, biological functional enrichment, and immune landscape analyses were performed to construct a nomogram and compare the results with those of previously published studies. A diagnostic model for CRLM was developed by employing ten ML techniques in cross-combination using genes from the prognostic model, followed by an analysis of immune microenvironmental differences between CRC and CRLM at the single-cell and spatial transcriptome levels. Key targets involved in CRC onset and CRLM progression were identified, and a transcription factor (TF) regulatory network was established by screening the upstream TFs of these targets. Fourteen phenotypic functions were scored to determine their associations with key targets. Molecular docking and dynamics simulations were performed to assess the binding affinity of CLG components with key targets (TP53, CDK1, and CCNB1). In vitro cell experiments verified the inhibitory effects of CLG and its components on colorectal cancer and their regulatory roles on critical targets. Results: The “active ingredient-target” network for CLG identified 248 intersecting targets. NMF clustering revealed two prognostic subtypes, i.e., C1 and C2, with C1 demonstrating superior prognostic outcomes over C2. Survival outcomes and immune differences between the subtypes were analyzed, resulting in the identification of 47 core targets by intersecting differentially expressed genes with previously identified targets. A CLG risk score-based prognostic ML model was constructed using ten ML cross-combination approaches and validated for survival prognosis, clinical diagnosis, and therapeutic utility across TCGA and GEO cohorts. Immune landscape analysis revealed that low-risk groups were characterized by a “hot tumor” phenotype with a favorable response to immunotherapy, whereas high-risk groups exhibited a “cold tumor” phenotype, with potential immunotherapy benefits. Independent prognostic analysis confirmed the CLG-derived risk score independently predicted prognosis. Validation against ten published models demonstrated elevated accuracy and efficacy for the proposed model. A CRLM diagnostic model was constructed using 11 genes from the prognostic model. Receiver operating characteristic (ROC) curve, calibration plot, and decision curve (DCA) analyses demonstrated its accuracy and clinical utility, indicating high predictive efficiency. Immune microenvironmental differences identified CDK1 and CCNB1 as potential biomarkers associated with CRLM onset and progression. CDK1 and CCNB1 expression levels had positive correlations with M2 macrophages, known to promote liver metastasis. The TF regulatory network revealed a regulatory relationship involving TP53, CDK1, and CCNB1, while gene set variation analysis (GSVA) demonstrated the associations of CDK1/CCNB1 with cell cycle regulation and apoptosis. Molecular docking and dynamics simulations revealed strong binding affinities of CLG components with key targets. In vitro experiments confirmed that CLG and its components effectively inhibit colorectal cancer and regulate critical gene expression. Conclusion: This study established an “active ingredient-target” network for CLG using network pharmacology and developed a dynamic “diagnosis-prognosis “model integrating ML based on drug targets. The clinical value of the model in CRC and CRLM patients was validated, and drug-target binding was elucidated using DL. It was discovered that CLG may inhibit CRC liver metastasis by targeting the TP53/CCNB1/CDK1 signaling pathway. These findings highlight the clinical utility of CLG and dynamic models in the prevention, diagnosis, and therapeutic management of CRC and CRLM, providing a robust foundation in bioinformatics, pharmacology, and potential targets for CRLM treatment in TCM, with broad clinical implications.