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Identification of hub genes and pathways in colitis-associated colon cancer by integrated bioinformatic analysis - PubMed

️Sat Jan 01 2022

Identification of hub genes and pathways in colitis-associated colon cancer by integrated bioinformatic analysis

Yongming Huang et al. BMC Genom Data. 2022.

Abstract

Background: Colitis-associated colon cancer (CAC) patients have a younger age of onset, more multiple lesions and invasive tumors than sporadic colon cancer patients. Early detection of CAC using endoscopy is challenging, and the incidence of septal colon cancer remains high. Therefore, identifying biomarkers that can predict the tumorigenesis of CAC is in urgent need.

Results: A total of 275 DEGs were identified in CAC. IGF1, BMP4, SPP1, APOB, CCND1, CD44, PTGS2, CFTR, BMP2, KLF4, and TLR2 were identified as hub DEGs, which were significantly enriched in the PI3K-Akt pathway, stem cell pluripotency regulation, focal adhesion, Hippo signaling, and AMPK signaling pathways. Sankey diagram showed that the genes of both the PI3K-AKT signaling and focal adhesion pathways were upregulated (e.g., SPP1, CD44, TLR2, CCND1, and IGF1), and upregulated genes were predicted to be regulated by the crucial miRNAs: hsa-mir-16-5p, hsa-mir-1-3p, et al. Hub gene-TFs network revealed FOXC1 as a core transcription factor. In ulcerative colitis (UC) patients, KLF4, CFTR, BMP2, TLR2 showed significantly lower expression in UC-associated cancer. BMP4 and IGF1 showed higher expression in UC-Ca compared to nonneoplastic mucosa. Survival analysis showed that the differential expression of SPP1, CFRT, and KLF4 were associated with poor prognosis in colon cancer.

Conclusion: Our study provides novel insights into the mechanism underlying the development of CAC. The hub genes and signaling pathways may contribute to the prevention, diagnosis and treatment of CAC.

Keywords: Colitis-associated colon cancer; Differentially expressed genes; Prognosis; Signaling pathways; functional enrichment analysis.

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Conflict of interest statement

TCGA and GEO belong to public databases. The patients involved in the database have obtained ethical approval. Our study is based on open source data, so there are no ethical issues and other conflicts of interest. There are no human subjects in this article and informed consent is not applicable.

The authors declare no conflict of interests.

Figures

**Fig. 1**
Normalized gene expression. The normalization of GSE44904 dataset (a and b). The normalization of GSE43338 dataset (c and d). Blue represents data before normalization, and red represents data after normalization

**Fig. 2**
Identification of DEGs from two dataset chips. Different groups in GSE44904 dataset: AOM/DSS VS Control group (a), AOM VS Control group (b), DSS VS Control group (c), and (d) GSE43338 dataset (CAC VS Control group). adj. P. Val < 0.05 and | log a fold change |< 2, red dots represent upregulated genes, green dots represent downregulated genes, and black dots represent genes with no significant difference. Heat maps of the top 100 DEGs in GSE44904 (e) and GSE43338 (f) datasets. Red indicates relative upregulation of gene expression; green indicates relative downregulation of gene expression. VENN diagram of DEGs identified from datasets (g&h: DEGs were only expressed in the AOM/DSS group from GSE44904 dataset; i&j: overlapping DEGs which were upregulated and downregulated in the two datasets)

**Fig. 3**
Protein–protein network and module analysis of DEGs. The network map of DEGs was constructed using STRING (a). The modular analysis was carried out on the network to screen out the module (b) with the highest score (MCODE score = 9.0). Red represents upregulated genes and the blue represents downregulated genes. Gene ontology (GO) enrichment analysis in DEGs and module genes were performed using the DAVID Database (c: DEGs, d: module genes); Classification: Biological Process (BP), B: Cellular Component (CC), C: Molecular Function (MF). KEGG pathways using the ggplot2 package in R language for visualization (e: DEGs, f: module genes). The size of the dot represents the amount of gene enrichment, and the color of the dot represents p value

**Fig. 4**
The hub genes were screened and analyzed by KEGG and correlation analysis. The top 11 genes with the most significance were selected as hub genes according to the score (a). KEGG pathway analysis of hub genes was analyzed by DAVID (b). The distribution relationship between hub genes and pathways (c): Red represents upregulated genes and blue represents downregulated genes. Correlation analysis of core TF and hub genes (d) and gene-miRNA interactions network (e), circles represents genes, diamonds represents TFs, and squares represents the miRNAs, sizes represents the degree

**Fig. 5**
The mRNA expression level of hub genes in patients according to the GEO database. UC-NonCa indicates nonneoplastic mucosa tissue of ulcerative colitis patients, and UC-Ca indicates ulcerative colitis-associated cancer tissue. ns, p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001

**Fig. 6**
Survival analysis of hub genes in colon cancer (P < 0.05). (a) CFTR, (b) KLF4, (C) SPP1

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Identification of hub genes and pathways in colitis-associated colon cancer by integrated bioinformatic analysis - PubMed