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. 2019 Oct 31:7:e7968.
doi: 10.7717/peerj.7968. eCollection 2019.

Identification of key genes and pathways associated with cholangiocarcinoma development based on weighted gene correlation network analysis

Jingwei Liu  1 , Weixin Liu  1 , Hao Li  1 , Qiuping Deng  1 , Meiqi Yang  1 , Xuemei Li  1 , Zeng Liang  1
Affiliations

Identification of key genes and pathways associated with cholangiocarcinoma development based on weighted gene correlation network analysis

Jingwei Liu et al. PeerJ. .

Abstract

Background: As the most frequently occurred tumor in biliary tract, cholangiocarcinoma (CCA) is mainly characterized by its late diagnosis and poor outcome. It is therefore urgent to identify specific genes and pathways associated with its progression and prognosis.

Materials and methods: The differentially expressed genes in The Cancer Genome Atlas were analyzed to build the co-expression network by Weighted gene co-expression network analysis (WGCNA). Gene ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted for the selected genes. Module-clinical trait relationships were analyzed to explore the association with clinicopathological parameters. Log-rank tests and cox regression were used to identify the prognosis-related genes.

Results: The most related modules with CCA development were tan module containing 181 genes and salmon module with 148 genes. GO analysis suggested enrichment terms of digestion, hormone transport and secretion, epithelial cell proliferation, signal release, fibroblast activation, response to acid chemical, wnt, Nicotinamide adenine dinucleotide phosphate metabolism. KEGG analysis demonstrated 15 significantly altered pathways including glutathione metabolism, wnt, central carbon metabolism, mTOR, pancreatic secretion, protein digestion, axon guidance, retinol metabolism, insulin secretion, salivary secretion, fat digestion. Key genes of SOX2, KIT, PRSS56, WNT9A, SLC4A4, PRRG4, PANX2, PIR, RASSF8, MFSD4A, INS, RNF39, IL1R2, CST1, and PPP3CA might be potential prognostic markers for CCA, of which RNF39 and PRSS56 also showed significant correlation with clinical stage.

Discussion: Differentially expressed genes and key modules contributing to CCA development were identified by WGCNA. Our results offer novel insights into the characteristics in the etiology, prognosis, and treatment of CCA.

Keywords: Cho; Cholangiocarcinoma; Prognosis; Progression; Weighted gene correlation network analysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Interaction relationship analysis of co-expression genes in cholangiocarcinoma.
Figure 2
Figure 2. The module–clinical trait relationships of genes involved in clinicopathological parameters of cholangiocarcinoma patients.
Figure 3
Figure 3. Gene Ontology analysis for genes in the hub modules of tan module and salmon module in cholangiocarcinoma.
Figure 4
Figure 4. The correlation between the expression levels of key genes of hub modules and the survival of CAA patients.
Figure 5
Figure 5. The correlation between the expression levels of key genes of hub modules and the survival of CAA patients.
(A) INS; (B) PPP3CA; (C) CST1; (D) RNF39; (E) PRSS56; (F) PRRG4; (G) SLC4A4; (H) PIR.
Figure 6
Figure 6. The differential expression of potential hub genes in different stages of CCA patients.
(A) Differential expression of INS, PPP3CA, CST1 and RNF39; (B) Differential expression of PRSS56, PRRG4, SLC4A4 and PIR.
See this image and copyright information in PMC

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