Abstract
Tumor recurrence is one of the most important risk factors that can negatively affect the survival rate of colorectal cancer (CRC) patients. However, the key regulators dictating this process and their exact mechanisms are understudied. This study aimed to construct a gene co-expression network to predict the hub genes affecting CRC recurrence and to inspect the regulatory network of hub genes and transcription factors (TFs). A total of 177 cases from the GSE17536 dataset were analyzed via weighted gene co-expression network analysis to explore the modules related to CRC recurrence. Functional annotation of the key module genes was assessed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The protein and protein interaction network was then built to screen hub genes. Samples from the Cancer Genome Atlas (TCGA) were further used to validate the hub genes. Construction of a TFs-miRNAs–hub genes network was also conducted using StarBase and Cytoscape approaches. After identification and validation, a total of five genes (TIMP1, SPARCL1, MYL9, TPM2, and CNN1) were selected as hub genes. A regulatory network of TFs-miRNAs-targets with 29 TFs, 58 miRNAs, and five hub genes was instituted, including model GATA6-MIR106A-CNN1, SP4-MIR424-TPM2, SP4-MIR326-MYL9, ETS1-MIR22-TIMP1, and ETS1-MIR22-SPARCL1. In conclusion, the identification of these hub genes and the prediction of the Regulatory relationship of TFs-miRNAs-hub genes may provide a novel insight for understanding the underlying mechanism for CRC recurrence.
Highlights
Colorectal cancer (CRC) ranks as the third most common type of tumor around the world and accounts for more than 7% of overall cancer-related death in China (Liu et al, 2015; Siegel et al, 2020)
Our results indicated that the module eigengenes (ME) of the turquoise module possessed the highest correlation with tumor recurrence [(P = 9 × 10−5, R2 = 0.29), Figure 3]
We showed that the module significance (MS) of the turquoise module was the highest among all modules (Supplementary Figure 4), which was considered to be more associated with tumor recurrence
Summary
Colorectal cancer (CRC) ranks as the third most common type of tumor around the world and accounts for more than 7% of overall cancer-related death in China (Liu et al, 2015; Siegel et al, 2020). With the recent developments in bioinformatics, a number of well-designed and effective methods are available for the comprehensive identification of biomarkers in cancer and the prediction of cancer-related signaling pathways (Carter et al, 2004; Carey et al, 2005; Liu et al, 2007). The weighted gene co-expression network analysis (WGCNA) approach provides a systematic biology strategy to identify modules of highly correlated genes and construct a co-expression gene network (Langfelder and Horvath, 2008). Compared with the other methodologies applied to analyze the high-throughput sequencing data, WGCNA implements methods for both weighted and unweighted correlation networks and provides a more effective mean to explore the potential association between modules and sample traits
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