A gene mutation-based risk model for prognostic prediction in liver metastases

Area of Science:

• Oncology
• Genomics
• Cancer Metastasis

Background:

• Liver metastasis presents a significant challenge in malignant tumor treatment.
• Genomic profiling is crucial for cancer diagnosis, treatment, and prognosis prediction.
• Developing predictive models for liver metastasis survival is essential.

Purpose of the Study:

• To construct and validate a gene mutation-based risk model for predicting survival in liver metastases.
• To investigate the relationship between the risk model, tumor microenvironment (TME), and somatic mutations in breast cancer liver metastases (BCLM).

Main Methods:

• A 10-gene risk model was developed using the Memorial Sloan-Kettering Cancer Center (MSKCC) dataset (800 patients).
• The model was validated across four independent cohorts (794 patients).
• Tumor microenvironment (TME) and somatic mutation analyses were performed on 51 BCLM patients with available genomic and RNA-sequencing data.

Main Results:

• The 10-gene risk model effectively stratified liver metastasis patients into high- and low-risk groups with distinct survival outcomes.
• Low-risk patients demonstrated significantly longer survival than high-risk patients in both training and validation cohorts.
• BCLM analysis revealed increased immune infiltration in the low-risk group and distinct mutation signatures between high- and low-risk groups.

Conclusions:

• The gene mutation-based risk model reliably predicts prognosis in liver metastases.
• Differences in TME and somatic mutations between risk groups offer insights for future research and clinical treatment strategies for liver metastases.