Construction of a co-expression network and prediction of metastasis markers in colorectal cancer patients with liver metastasis

Abstract

Background: Colorectal cancer (CRC) is a common global malignancy associated with high invasiveness, high metastasis, and poor prognosis. CRC commonly metastasizes to the liver, where the treatment of metastasis is both difficult and an important topic in current CRC management.Methods: Microarrays data of human CRC with liver metastasis (CRCLM) were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database to identify potential key genes. Differentially expressed (DE) genes (DEGs) and DEmiRNAs of primary CRC tumor tissues and metastatic liver tissues were identified. Microenvironment Cell Populations (MCP)-counter was used to estimate the abundance of immune cells in the tumor micro-environment (TME), and weighted gene correlation network analysis (WGCNA) was used to construct the co-expression network analysis.Gene Ontology and Kyoto Encyclopaedia of Gene and Genome (KEGG) pathway enrichment analyses were conducted, and the protein-protein interaction (PPI) network for the DEGs were constructed and gene modules were screened.Results: Thirty-five pairs of matched colorectal primary cancer and liver metastatic gene expression profiles were screened, and 610 DEGs (265 up-regulated and 345 down-regulated) and 284 DEmiRNAs were identified. The DEGs were mainly enriched in the complement and coagulation cascade pathways and renin secretion. Immune infiltrating cells including neutrophils, monocytic lineage, and cancer-associated fibroblasts (CAFs) differed significantly between primary tumor tissues and metastatic liver tissues. WGCN analysis obtained 12 modules and identified 62 genes with significant interactions which were mainly related to complement and coagulation cascade and the focal adhesion pathway. The best subset regression analysis and backward stepwise regression analysis were performed, and eight genes were determined, including F10, FGG, KNG1, MBL2, PROC, SERPINA1, CAV1, and SPP1. Further analysis showed four genes, including FGG, KNG1, CAV1, and SPP1 were significantly associated with CRCLM.Conclusions: Our study implies complement and coagulation cascade and the focal adhesion pathway play a significant role in the development and progression of CRCLM, and FGG, KNG1, CAV1, and SPP1 may be metastatic markers for its early diagnosis.

Identification of ROS and KEAP1‐related genes and verified targets of α‐hederin induce cell death for CRC

“…CAV1 has been identified as a potential biomarker for CRC metastasis (Lin et al, 2022;Subbarayan et al, 2022) and is significantly associated with CRC with liver metastasis (CRCLM) in CRC, as well as worse survival rates (Dai et al, 2023;Ng et al, 2022).…”

“…CAV1 has been identified as a potential biomarker for CRC metastasis (Lin et al, 2022; Subbarayan et al, 2022) and is significantly associated with CRC with liver metastasis (CRCLM) in CRC, as well as worse survival rates (Dai et al, 2023; Ng et al, 2022). CAV1 expression is also linked to CRC migration and invasion, and its upregulation in HCT116 cell lines was found to promote cell proliferation and invasion, (Ng et al, 2022) while inhibition of CAV1 in CRC cell lines significantly reduced these processes.…”

New treatment alternatives for primary and metastatic colorectal cancer by an integrated transcriptome and network analyses

“…The common genes were clustered on the STRING network; core genes of modules were analyzed in terms of survival effects and protein levels. A study considered samples of CRC with liver metastasis to identify metastasis-related pathways 15 . They applied DEG analysis and WGCNA, finally revealing the roles of the complement-coagulation cascade and the focal adhesion pathway in CRC progression.…”

Establishing a carcinoembryonic antigen-associated competitive endogenous RNA network and forecasting an important regulatory axis in colon adenocarcinoma patients

“…Elucidating the molecular mechanisms of the development and progression of colon cancer and identifying promising biomarkers are critical to the identification of novel therapeutic targets and improvement of patient outcomes ( 14 ). Considering the critical role of CEACAM5, we endeavor to construct a CEACAM5-associated ceRNA network utilizing bioinformatics techniques, with the objective of identifying potential promising biomarkers or therapeutic targets.…”