Identification and analysis of immune microenvironment characterization and key signal pathways in liver metastasis.

Abstract

e15004 Background: The liver represents a common site for metastatic disease, patients with advanced malignancies were usually associated with worse prognostic outcomes once liver metastasis has occurred. The aim of this study was to explore the immune microenvironment characterization and key signal pathways of liver metastasis in colorectal cancer (CRC) and breast cancer (BRCA), and to provide potential targets and decision-making basis for the diagnosis and treatment of patients with liver metastasis. Methods: Two datasets (GSE49355 and GSE58708) were downloaded from the Gene Expression Omnibus (GEO) database, and patients with primary tumors and paired liver metastases were enrolled. Data were processed by the limma and Deseq2 package and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO), Kyoto Enrichment of Genes and Genomes (KEGG) and Reactome pathway analysis was performed using clusterProfiler package. Prognosis markers were identified via survival analysis of The Cancer Genome Atlas (TCGA) databases. CIBERSORT algorithm was used to calculate the proportions of 22 types of infiltrated immune cells. We used Spearman’s correlation analysis to describe the correlation between gene expression and microsatellite instability (MSI) or tumor mutational burden (TMB) score. Results: We investigated 16 patients with paired specimens (3 patients with BRCA and 13 patients with CRC). A total of 424 and 49 DEGs were identified in the BRCA and CRC samples. Only 26 DEGs were shared by the two kinds of cancers. The functional enrichment analysis suggested that the functions of DEGs were mainly related to cholesterol metabolism, complement and coagulation cascades, platelet activation etc. The survival analysis indicated that high expression levels of SERPINA3 was associated with a better prognosis in TCGA-BRCA (P = 0.01), while high ITIH2 and COLEC11 expression correlated with poor prognosis in TCGA-COAD (P = 0.045 and P = 0.013, respectively). CIBERSORT analysis revealed a significantly increased proportion of M2-macrophages and decreased proportion of activated mast cells in liver metastases. The Spearman correlation analysis showed that the SERPINA3 expression had a significantly negative relationship with the TMB (r = -0.26, P＜0.001) in TCGA-BRCA, but not significantly associated with the MSI (r = -0.02, P = 0.520), while the COLEC11 expression was significantly correlate negatively with the MSI and TMB (r = -0.15, P < 0.001 and r = -0.23, P < 0.001, respectively) in TCGA-COAD. Conclusions: The immune microenvironment of liver metastases exhibits a significant increase in the proportion of M2-macrophages and a significant decrease in the proportion of activated mast cells, and the key signal pathways implicate cholesterol metabolism and platelet activation. The specific mechanism needs to be further elucidated. Support: 81972853, 81572279, LC2019ZD009.