Transplant for colorectal cancer liver metastases

Liver Transplantation for Colorectal Liver Metastases

Colorectal cancer is one of the most common malignancies worldwide. Approximately half of the patients diagnosed will develop colorectal liver metastases (CRLM). Liver resection has a 50% 5-year survival; however, only a fourth of cases are resectable. Unresectable CRLM has poor prognosis despite improved systemic and local ablative treatments. Liver transplantation (LT) has demonstrated a survival benefit in initial prospective clinical trials. Current use of LT for CRLM is limited to several randomized trials and high-performing centers. Improving patient selection criteria and perioperative management, LT will likely become an important part of the multidisciplinary approach to managing the metastatic disease.

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi-omics approach based on epithelial-mesenchymal transition-related genes

The progression and the metastatic potential of colorectal cancer (CRC) are intricately linked to the epithelial-mesenchymal transition (EMT) process. The present study harnesses the power of machine learning combined with multi-omics data to develop a risk stratification model anchored on EMT-associated genes. The aim is to facilitate personalized prognostic assessments in CRC. We utilized publicly accessible gene expression datasets to pinpoint EMT-associated genes, employing a CoxBoost algorithm to sift through these genes for prognostic significance. The resultant model, predicated on gene expression levels, underwent rigorous independent validation across various datasets. Our model demonstrated a robust capacity to segregate CRC patients into distinct high- and low-risk categories, each correlating with markedly different survival probabilities. Notably, the risk score emerged as an independent prognostic indicator for CRC. High-risk patients were characterized by an immunosuppressive tumor milieu and a heightened responsiveness to certain chemotherapeutic agents, underlining the model's potential in steering tailored oncological therapies. Moreover, our research unearthed a putative repressive interaction between the long non-coding RNA PVT1 and the EMT-associated genes TIMP1 and MMP1, offering new insights into the molecular intricacies of CRC. In essence, our research introduces a sophisticated risk model, leveraging machine learning and multi-omics insights, which accurately prognosticates outcomes for CRC patients, paving the way for more individualized and effective oncological treatment paradigms.