Weighted gene coexpression network analysis reveals hub genes involved in cholangiocarcinoma progression and prognosis

Tian, Anmin; Pu, Ke; Li, Boxuan; Li, Min; Liu, Xiaoguang; Gao, Liping; Mao, Xiaorong

doi:10.1111/hepr.13386

Cited by 19 publications

(16 citation statements)

References 41 publications

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“…Until now, our understanding of the complex mechanisms of CCA is still limited. One study using CCA data also analyzed the genes implicated in CCA (Tian et al, 2019). They dissected the genome network by other bioinformatic tool to explore the protein–protein interaction.…”

Section: Discussionmentioning

confidence: 99%

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

Liu

et al. 2019

PeerJ

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BackgroundAs 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 MethodsThe 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.ResultsThe 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.DiscussionDifferentially 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.

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Section: Discussionmentioning

confidence: 99%

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

Liu

et al. 2019

PeerJ

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“…These pathways are well documented as participating in the carcinogenesis and progression of CCA [36]. Various studies based on multiple CCA independent cohorts [37][38][39] also detected that metabolic-related biological processes including small molecule and lipid metabolic processes relate to energy metabolism were pivotal for CCA development. Increasing evidence has demonstrated that fatty acids synthesis related genes (FASN and SLC27A1) [40,41], fatty acid transport proteins (FATP2, FATP1, FATP5, and CD36), and fatty acid binding proteins (FABP1, FABP4, and FABP5) [42] contribute to CCA carcinogenesis.…”

Section: Discussionmentioning

confidence: 96%

An Integrated mRNA-lncRNA Signature for Overall Survival Prediction in Cholangiocarcinoma

Zeng

Mukiibi

et al. 2020

Preprint

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BackgroundThe incidence and mortality rate of cholangiocarcinoma (CCA) have been rising globally. Patients with CCA have extremely poor prognosis, partly due to the silent clinical character and hence diagnosed at advantage stage without effective treatments. There is growing evidence showing that aberrant expression of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) are involved in tumorigenesis and development of CCA. It is essential to establish an integrated mRNA-lncRNA signature to improve the ability of prognostic prediction in CCA patients.MethodsWe collected a training dataset of 45 patients from The Cancer Genome Atlas dataset and a validation cohort (GSE107943) of 57 patients from Gene Expression Omnibus. An integrated mRNA-lncRNA risk score was established by a univariate and a multivariate Cox regression analyses. Time-dependent receiver operating characteristic (ROC) analysis was used to evaluate prognostic performance. Moreover, we conducted a correlation analysis between the signature and different clinical characteristics, and preformed weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis to investigate functional roles of the integrated signature.ResultsA total of two mRNAs (CFHR3 and PIWIL4) and two lncRNAs (AC007285.1 and AC134682.1) were identified to construct the integrated signature through a univariate Cox regression (P-value = 1.35E-02) and a multivariable Cox analysis (P-value = 1.12E-02). The ROC curve suggested the integrated mRNA-lncRNA signature possessed a high specificity and sensitivity of prognostic prediction with an area under the curve (AUC) of 0.872 and 0.790 at 1-year and 3-years, respectively. Subsequently, the signature was validated in GSE107943 cohort and combined dataset, and an area under the ROC curve reached up to 0.750 and 0.819 at 1-year. The signature was not only independent from different clinical features (P-value= 1.12E-02), but also outperformed other clinical characteristics as prognostic biomarkers with AUC of 0.781 at 3 years. These molecules in the integrated signature may associated with metabolic-related biological process and lipid metabolism pathway, which was highly involved in CCA carcinogenesis. ConclusionThese results showed that the integrated mRNA-lncRNA signature had an independent prognostic value for risk stratification, and further facilitated personalized treatment for CCA patients.

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“…To further evaluate the performance of the Fisher score algorithm, a series of control feature selection algorithms were utilized to select feature genes from the current integrated HCC dataset. The algorithms for comparison included WGCNA 54 , Lasso 55 , ReliefF 56 and random forest 57 .…”

Section: Methodsmentioning

confidence: 99%

Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

2019

Sci Rep

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This study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

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Weighted gene coexpression network analysis reveals hub genes involved in cholangiocarcinoma progression and prognosis

Cited by 19 publications

References 41 publications

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

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

An Integrated mRNA-lncRNA Signature for Overall Survival Prediction in Cholangiocarcinoma

Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

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