Impact of neoadjuvant chemotherapy on post-hepatectomy regeneration for patients with colorectal cancer liver metastasis - Systematic review and meta-analysis

The Impact of Chemotherapy and Transforming Growth Factor-β1 in Liver Regeneration after Hepatectomy among Colorectal Cancer Patients

An ongoing debate surrounds the impact of chemotherapy on post-hepatectomy liver regeneration in patients with colorectal cancer liver metastases (CRLM), with unclear regulatory mechanisms. This study sought to delve into liver regeneration post-resection in CRLM patients, specifically examining the roles of hepatocyte growth factor (HGF) and transforming growth factor β1 (TGF-β1). In this longitudinal observational study, 17 patients undergoing major liver resection for CRLM and 17 with benign indications as controls were enrolled. Liver regeneration within 30 postoperative days was assessed via CT, considering clinicopathological characteristics, liver enzymes, liver stiffness by elastography, and the impact of HGF and TGF-β1 on liver regeneration. The results revealed that the control group exhibited significantly higher mean liver regeneration volume (200 ± 180 mL) within 30 days postoperatively compared to the CRLM group (72 ± 154 mL); p = 0.03. Baseline alkaline phosphatase (AP) and TGF-β1 blood levels were notably higher in the CRLM group. Immunohistochemical analysis indicated a higher proportion of CRLM patients with high TGF-β1 expression in liver tissues compared to the control group (p = 0.034). Correlation analysis showed that resected liver volume, baseline plasma HGF, AP, and albumin levels significantly correlated with liver regeneration volume. However, in multivariable analysis, only resected liver volume (β: 0.31; 95% CI: 0.14-0.47, p = 0.01) remained significant. In conclusion, this study highlights compromised liver regeneration in CRLM patients post-chemotherapy. Additionally, these patients exhibited lower serum TGF-β1 levels and reduced TGF-β1 expression in liver tissue, suggesting TGF-β1 involvement in mechanisms hindering liver regeneration capacity following major resection after chemotherapy.

Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation

Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. 11 genes were commonly selected by LASSO and P-SVM algorithms, among which seven had prognostic value in colorectal cancer. It was found that the expression of the MMP3 gene decreases in stage IV of colorectal cancer compared to other stages (P value < 0.01). Also, the expression level of the WNT11 gene was observed to increase significantly in this stage (P value < 0.001). It was also found that the expression of WNT5a, TNFSF11, and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into identifying biomarkers for CRC metastasis and may potentially lay the groundwork for innovative therapeutic strategies for treatment of this disease.