Abstract
Metastasis and recurrence are major causes of colorectal cancer (CRC) death, but their molecular mechanisms are unclear. In this study, genes associated with CRC metastasis and recurrence were identified by weighted gene coexpression network analysis, selecting the top 25% most variant genes in the dataset GSE33113. By average linkage hierarchical clustering, a total of 21 modules were generated. One key module was identified as the most relevant to the prognosis of CRC. Gene Ontology analysis indicated that genes associated with tumor metastasis and recurrence in this module were significantly enriched in inflammatory biological functions. Functional analysis was performed on the key module, and candidate hub genes (ADAM8, LYN, and S100A9) were screened out by expression and survival analysis. In summary, the three core genes identified in this study could greatly improve our understanding of CRC metastasis and recurrence. The results also provide a theoretical basis for the use of three core genes (ADAM8, LYN, and S100A9) as a combined marker for early diagnosis, which could benefit CRC patients.
Highlights
Colorectal cancer (CRC) is the most common gastrointestinal malignancy and one of the leading causes of cancer death worldwide [1, 2]
We used weighted gene coexpression network analysis (WGCNA) to construct a coexpression network and identify modules related to clinical traits in order to determine the core genes [6]
ADAM8, C5AR1, IL6, LYN, S100A8, S100A9, and S100A12 were all found to be involved in signaling pathways related to the inflammatory response, suggesting that they play a key part in this response
Summary
Colorectal cancer (CRC) is the most common gastrointestinal malignancy and one of the leading causes of cancer death worldwide [1, 2]. Despite the availability of multiple therapies, patients with CRC are still at high risk of recurrence and metastasis. Further investigation of the molecular mechanisms underlying CRC is required, in order to identify the key genes regulating metastasis and recurrence. This will be of great significance for developing the early diagnosis, treatment, and prognosis of cancer [5]. Correlation networks are used increasingly often for bioinformatics applications. Network-based approaches include weighted gene coexpression network analysis (WGCNA), which is a systems biology method for describing the correlation patterns among genes across microarray samples. WGCNA is used to find clusters (modules) of highly correlated genes and clusters of clinical features; these clusters GSM820102
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