Identification of hub genes in liver hepatocellular carcinoma based on weighted gene co-expression network analysis

doi:10.21203/rs.3.rs-2457455/v1

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Identification of hub genes in liver hepatocellular carcinoma based on weighted gene co-expression network analysis

https://doi.org/10.21203/rs.3.rs-2457455/v1

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published 29 Apr, 2024

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Methods: Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells was explored by TIMER.

Results: A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine−cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, and worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity.

Conclusion: CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

weighted gene co-expression network analysis

bioinformatics analysis

liver hepatocellular carcinoma

hub gene

protein–protein interaction

Liver hepatocellular carcinoma (LIHC) is one of the most common malignancies worldwide, ranking sixth for incidence and third for mortality (Kulik & El-Serag, 2019; Sung et al., 2021). According to the global cancer statistics report, LIHC is responsible for approximately 906,000 new cases and 830,000 deaths in 2020(Sung et al., 2021). Chronic infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), aflatoxin-contaminated foods, heavy alcohol intake, excess body weight, type 2 diabetes, and smoking are main risk factors for LIHC (Kulik & El-Serag, 2019; Yang et al., 2019). The commonly used therapies for LIHC include hepatectomy, liver transplantation and ablative therapy. Although there are many new methods for the diagnosis and treatment of LIHC, the prognosis is still poor due to high rates of recurrence and metastasis. An important reason for poor prognosis is the lack of an accurate prognosis grading system and an effective early screening method for LIHC. Therefore, it is reasonable to evaluate the value of potential biomarkers in the diagnosis and treatment of LIHC.

With the development of genome technology, a variety of tools(Langfelder & Horvath, 2008; Newman et al., 2015; van Ijzendoorn et al., 2019) have been used for the study of the molecular mechanism of diseases and biomarker identification. Weighted gene co-expression network analysis (WGCNA) is a novel bioinformatics approach used to describe patterns of gene association between different samples. It can cluster genes and form modules by similar gene expression patterns and analyze the relationship between modules and specific features to identify candidate biomarker genes (Pei, Chen, & Zhang, 2017). WGCNA is helpful in understanding the molecular mechanisms of cancer and identifying reliable biomarkers for more effective diagnosis, prognosis and treatment.

In this study, we screened the key central genes associated with LIHC integrately, which provides a reliable basis for the analysis of differential co-expressed genes to understand the etiology and potential molecular events of LIHC.

Data Processing from TCGA and GEO Database

In order to screen the differential expression genes between LIHC and normal control, we obtained the data of gene expression profiles in TCGA (https://portal.gdc.cancer.gov/projects/TCGA-LIHC) (including 50 normal tissues and 374 tumor tissues) and GEO (https://www.ncbi.nlm.nih.gov/gds) GSE54236 (including 77 adjacent nontumorous samples and 78 LIHC samples; platform: GPL6480), After removing repetitive probes of the same gene, an interpreted gene expression profile of 18,076 genes was obtained for the following analysis.

Co-expression Network Construction by WCGNA

The WGCNA package in R was employed to construct a gene co-expression network using the gene expression data profiles of TCGA-LIHC and GSE54236 (Chen et al., 2017; Langfelder & Horvath, 2008). The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software(Langfelder & Horvath, 2008). First, the expression levels of individual transcripts were transformed to generate a similarity matrix based on Pearson correlation values between paired genes. Then, the similarity matrix is transformed into the adjacency matrix. β = 3 and 20 were selected as the soft threshold power. The adjacency matrix is made from the following formula: \({a}_{ij}=| {c}_{ij} {|}^{\beta }\) Next the adjacency matrix transformed to corresponding topological overlap matrix (TOM). The genes were divided into different modules by dynamic mixed cutting method, and the minimum module size cut-off value of 30 was simulated.

Identification of DEGs and Validation of Co-expression Modules

To explore differentially expressed genes (DEGs) in TCGA-LIHC and GSE54236 data sets, the R-package limma, an integrated solution of analysis for RNA-Sequencing and microarray data, was used to filter out DEGs in LIHC(C. Y. Li, Cai, Tsai, & Wang, 2020). |logFC| ≥1.0 and P < 0.05 were defined for screening DEGs. A volcano plot using the R package ggplot2 was constructed to reveal the differentially expressed genes. The overlapped genes between the DEGs extracted from the co-expression network were presented by Venn diagram.

Function Enrichment Analysis

In order to conduct the Genetic Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, R Package clusterProfiler was used to explore the functions between the selected genes("The Gene Ontology Resource: 20 years and still GOing strong," 2019). Statistical significance of GO terms or KEGG pathways was considered as P < 0.05. The GO annotation contains biological process (BP), cellular component (CC) and molecular function (MF).

Construction of PPI network and Screening of Hub Genes

To construct a PPI network of the identified differentially expressed genes (DEGs), the online database Search Tool for the Retrieval of Interacting Genes (STRING)(Szklarczyk et al., 2019) was used. Genes with confidence score ≥ 0.4 were chosen to build a network model by Cytoscape visualization software (v3.7.2)(Doncheva, Morris, Gorodkin, & Jensen, 2019; Shannon et al., 2003). Maximal Clique Centrality (MCC), an effective way to find a hub node, was calculated by CytoHubba (Chin et al., 2014), and the 10 genes with the highest MCC values were identified as the hub genes.

Validation and Survival Analysis of Hub Genes

First, we used GEPIA2 (http://gepia2.cancer-pku.cn/) (Tang, Kang, Li, Chen, & Zhang, 2019) to verify the expression pattern of hub genes in LIHC tissues and normal tissues from TCGA database. Gene expression data of liver cancer patients with clinical information was downloaded from the International Cancer Genome Consortium (ICGC) (http://icgc.org/), and analyzed to validate expression level of hub genes. Based on data from TCGA database, LIHC patients were divided into two groups according to the median expression level of hub gene. Then Kaplan-Meier survival analysis was performed using survival package in R software to detect the relationship between overall survival (OS) and hub genes. In addition, the relationship between disease-free survival (DFS) and hub gene expression in LIHC patients was determined with the online tool GEPIA2 (Tang et al., 2019). P < 0.05 was regarded as the judgment criterion of statistically significant survival-related hub genes.

Immune Infiltration Analysis TIMER

To find the correlation between the expression of the hub genes and tumor infiltrating immune cell subtypes including B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells, we utilized the online comprehensive tool Tumor Immune Estimation Resource (TIMER, http://timer.cistrome.org/), an online systematic analysis database containing 10,897 samples across 32 cancer types from the TCGA database (B. Li et al., 2016; T. Li et al., 2017; T. Li et al., 2020).

Weighted Gene Co-expression Modules Construction

Using the WGCNA package, we constructed the gene expression data profiles of TCGA-LIHC and GSE54236 to gene co-expression networks. In the present study, each module was represented by a color, we emerged 9 modules in the TCGA-LIHC (Figure 1A) and 27 modules in the GSE54236 (Figure 2A) from the analysis. The results obtained from the preliminary analysis of the module-trait relationships showed that the blue module in the TCGA-LIHC (Figure 1B) and brown module in the GSE54236 (Figure 2B) were found to have the strongest association with normal tissues (blue module: r = 0.77, p = 3e-85; brown module: r = 0.57, p = 2e-15).

Identification of Genes Between the DEGs and Co-expression Modules

We identified 2704 DEGs in TCGA-LIHC (Figure 3A) and 691 DEGs in GSE54236 (Figure 3B) using volcano plot. And 3103 and 1450 co-expression genes were separately selected in the blue module of TCGA-LIHC and brown module of GSE54236. A heatmap is a simple yet effective way to compare the content of multiple major gene lists. The heatmap showed the distribution of the top 100 DEGs in TCGA-LIHC and GSE54236 (Figure 3C, D). To overlap the gene in two co-expression modules and DEG list, a total of 68 overlapping genes were performed by Venn diagram (Figure 3E).

Functional Enrichment Analyses

To reveal the potential functions of the 68 genes, we divided genes into several enriched gene sets based on the Gene Ontology (GO) analysis. In the biological process (BP) analysis, the 68 genes mainly involved in complement activation. In the result of the cellular component (CC), these genes were mainly enriched in collagen trimer. The molecular function (MF) of 68 genes was mainly enriched in carbohydrate binding and receptor ligand activity (Figure 4A). Meanwhile we conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, showing that cytokine−cytokine receptor interaction was widely related to the 68 genes (Figure 4B).

Construction of the PPI Network

In order to find the hub genes, we performed the PPI analysis of overlapped genes the utilized the “String” website tool (Figure 5A). Among the 11 topological analysis methods provided by CytoHubba, MCC performed better in predicting the accuracy of essential proteins from PPI networks. Using the MCC algorithm of CytoHubba plugin, the top 10 highest-scored genes, including CFP (Complement Factor Properdin), CLEC1B (C-type lectin domain family 1 member B), CLEC4G (C-type lectin domain family 4 member G), CLEC4M (C-type lectin domain family 4 member M), FCN2(Ficolin 2), FCN3(Ficolin 3), LYVE1(Lymphatic Vessel Endothelial Hyaluronan Receptor 1), MARCO(Macrophage Receptor With Collagenous Structure), PAMR1(Peptidase Domain Containing Associated With Muscle Regeneration 1), TIMD4(T cell immunoglobulin and mucin domain containing 4) were selected as the hub genes from the PPI network (Figure 5B).

Validation and Survival Analysis of Hub Genes and Expression of Hub Genes

Then we verified the expression levels of the hub genes among patients from the TCGA and ICGC database. All 10 hub genes were found to be significantly downregulated in LIHC compared to normal tissue (Figure 6, Table 1). The OS and DFS analyses were performed by Kaplan Meier plot to identify the effect of hub genes on the prognosis from PPI network complexes and WGCNA modules. The results showed that low expression of CFP (Figure 7A), CLEC1B (Figure 7B), CLEC4G (Figure 7C), CLEC4M (Figure 7D), FCN2 (Figure 7E), FCN3 (Figure 7F), PAMR1 (Figure 7I) and TIMD4 (Figure 7J) was associated with poor OS in LIHC patients (P < 0.05). Besides, among the hub genes, the lower expression level of CFP (Figure 8A), CLEC1B (Figure 8B), FCN3 (Figure 8F) and TIMD4 (Figure 8J) was significantly associated with worse DFS of LIHC patients (P < 0.05).

Table1 Expression of the Hub gene in LIHC and adjacent tissues from the ICGC database

Gene	Type	Expression log2(TPM+1)	P value
CFP	LIHC	1.49±0.89	8.95E-98
	Adjacent	3.89±0.76
CLEC1B	LIHC	0.59±0.96	4.41E-107
	Adjacent	3.88±1.05
CLEC4G	LIHC	0.75±1.18	1.20E-125
	Adjacent	4.71±0.98
CLEC4M	LIHC	0.52±1.14	8.52E-110
	Adjacent	4.16±1.10
FCN2	LIHC	0.96±1.30	4.77E-115
	Adjacent	4.90±1.05
FCN3	LIHC	2.091±1.69	1.03E-101
	Adjacent	6.426±1.13
LYVE1	LIHC	1.26±0.93	1.95E-62
	Adjacent	3.42±1.10
MARCO	LIHC	1.19±1.42	7.06E-80
	Adjacent	4.88±1.51
PAMR1	LIHC	0.83±0.63	2.70E-61
	Adjacent	2.10±0.62
TIMD4	LIHC	0.74±0.84	9.22E-40
	Adjacent	2.23±1.06

Association between Hub Genes with Immune Infiltration Level

Infiltrating immune cells are an important part of tumor microenvironment besides tumor cells and stromal cells. The online search tool TIMER was utilized to explore probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells. All those 10 hub genes had positive association with tumor purity (Figure 9), while no or weak correlation with B cell infiltration, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells (Figure 9).

LIHC remains one of the most malignant cancers with poor prognosis. WGCNA has been used to explore biomarkers related to pathogenesis, diagnosis and prognosis of LIHC. Immune cell infiltration related biomarkers in LIHC including CCL5, CXCR6, CD3E and LCK were identified using WGCNA(Bai et al., 2020). Another study found that 13 hub genes (GTSE1, PLK1, NCAPH, SKA3, LMNB2, SPC25, HJURP, DEPDC1B, CDCA4, UBE2C, LMNB1, PRR11, and SNRPD2) had high correlation with histologic grade in LIHC by analyzing TCGA LIHC dataset(Gu et al., 2020). While our study provides a more comprehensive and innovative idea from various sources of data.

WGCNA, a robust and reliable search tool for large-scale data analysis, was used to find effective biological mechanisms and key genes from gene expression microarrays in this study. By comprehensive bioinformatics analysis, a total of 68 significant DEGs were identified in TCGA-LIHC and GSE54236 datasets. Besides, the genes were clustered mostly in the functional set associated with complement activation, collagen trimer, receptor ligand activity, cytokine−cytokine receptor interaction. The complement system is an ancient defense mechanism preceding adaptive immunity, and complement activation is an important component of tumor-promoting inflammation(Afshar-Kharghan, 2017). Chronic infection with HBV or HCV is main risk for LIHC, and tumor-promoting effect of complement activation plays a role in the progression of inflammation to tumor. Cytokine relevant pathways and ligand-receptor relevant pathways participate in the pathogenesis of early-stage LIHC. Collagen is a major component of the extracellular matrix (ECM), collagen trimer is a biomarker of cancer metastasis (Xu et al., 2019). By PPI network construction, 10 hub genes including CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, LYVE1, MARCO, PAMR1, TIMD4 were identified. The expression of these 10 hub genes in LIHC was down-regulated compared with the normal control group. The down-regulated expression of CFP, CLEC1B, FCN3 and TIMD4 was significantly correlated with poor OS and DFS of LIHC. Through the study of TIMER, we found that the 10 hub genes were mainly expressed in LIHC cells instead of immune cells, and were absent in the immune regulation of tumor microenvironment. These findings may support CFP, CLEC1B, FCN3 and TIMD4 as new therapeutic targets of LIHC and may contribute to the assessment of prognostic biomarkers.

In our study, CFP, CLEC1B, FCN3 and TIMD4 were down-regulated in LIHC tissues compared with normal tissues, and could be strong predictors of poor outcome in LIHC. The relationship between the four genes and cancer has been confirmed by several previous studies. CFP (Complement Factor Properdin) is a protein coding gene, and can positively regulate the alternative complement pathway of the innate immune system yielding the elimination of pathogens, apoptotic and necrotic cells (Kemper, Atkinson, & Hourcade, 2010; Kemper, Mitchell, Zhang, & Hourcade, 2008). Several studies reported the tumor suppressive effect of CFP on melanoma, breast, stomach and lung cancer(Al-Rayahi, Browning, & Stover, 2017; Block et al., 2019; Cui et al., 2021). In stomach and lung cancer, the expression level of CFP was lower than in normal tissues, and low expression level of CFP was associated with poor prognosis (Cui et al., 2021). CLEC1B (C-type lectin domain family 1 member B) is a novel platelet-associated molecule secreted by activated platelets around tumors(Meng, Luo, & Liu, 2021). CLECB1 has been confirmed its inhibitory effect on platelet aggregation and tumor metastasis in colon cancer (Suzuki-Inoue et al., 2007). CLEC1B has been reported to be significantly down-regulated in liver cancer (Critelli et al., 2017). FCN3 (Ficolin 3) is a secreted lectin that activates the complement pathway (Endo, Matsushita, & Fujita, 2011). FCN3 expression was significantly down-regulated in lung cancer tissues compared with matched normal lung tissues, low expression levels of FCN3 have been described as prognostic biomarker for cancer (Jang et al., 2021). TIMD4 (T cell immunoglobulin and mucin domain containing 4) is a phosphatidylserine receptor that enhances the engulfment of apoptotic cells, and is involved in regulating T-cell proliferation and lymphotoxin signaling (Freeman, Casasnovas, Umetsu, & DeKruyff, 2010). As opposed to LIHC, TIMD4 is overexpressed in lung cancer and colorectal cancer, which is correlated with poor prognosis (Dorfman, Hornick, Shahsafaei, & Freeman, 2010; Tan, Zhang, Yao, & Shen, 2018).

We admit that this study has some limitations and deficiencies. First, this research mainly focuses on data mining and data analysis based on methodology, and the results have not been verified by experiments. Further experiments are needed to better confirm the findings of this study. Our research also has certain limitations for the classification of different subtypes of tumors. Although our comprehensive bioinformatics analysis identifies potential diagnostic genes for LIHC, it may be biased for patients with different LIHC subtypes.

By WGCNA and other analytical methods (GO, KEGG, PPI network, survival analysis and TIMER), the current study suggests that CFP, CLEC1B, FCN3 and TIMD4 may be novel potential molecular targets for prognosis prediction in LIHC for the first time.

Ethics approval Not applicable

Consent to participate Not applicable

Consent for publication Not applicable

Conflict of interest The authors declare no competing interests

Data Availability

The datasets generated for this study can be found in the Publicly available datasets were analyzed in this study. This data can be found at: TCGA-LIHC (https://portal.gdc.cancer.gov/projects/TCGA-LIHC), GSE54236 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54236), ICGC-LIRI-JP(https://dcc.icgc.org/projects/LIRI-JP).

Author contributions

(I) Conception and design: JWS, ZZZ, JRC

(II) Collection and assembly of data: ZZZ

(III) Data analysis and interpretation: JWS, JRC

(IV) Manuscript writing: All authors

(V) Final approval of manuscript: XPL, XLX

Funding Statement

This work was supported in part by Natural Science Foundation of Zhejiang Province of China under Grant No. LQ23H030001 and Zhejiang Shuren University Science Foundation of China under Grant No. KXJ1720102 .

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Identification of hub genes in liver hepatocellular carcinoma based on weighted gene co-expression network analysis

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Journal Publication

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Abstract

Figures

Introduction

Methods

Data Processing from TCGA and GEO Database

Co-expression Network Construction by WCGNA

Identification of DEGs and Validation of Co-expression Modules

Function Enrichment Analysis

Construction of PPI network and Screening of Hub Genes

Validation and Survival Analysis of Hub Genes

Immune Infiltration Analysis TIMER

Results

Discussion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1