Identifying gene modules of thyroid cancer associated with pathological stage by weighted gene co-expression network analysis

Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single-gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co-expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta-analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co-expressed module: a 'PTC-module'. Using independent data, we demonstrated the high prognostic capacity of the PTC-module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC-module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4-demethoxydaunorubicin hydrochloride, AS605240, BRD-A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co-expression analysis to gain a systems-level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.

Overexpression of SH2D1A promotes cancer progression and is associated with immune cell infiltration in hepatocellular carcinoma via bioinformatics and in vitro study

BACKGROUND

SH2 domain containing 1A (SH2D1A) expression has been linked to cancer progression. However, the functions of SH2D1A in hepatocellular carcinoma (HCC) have not been reported.

METHODS

The effects of SH2D1A on the proliferation, migration, and invasion of HCC cells and the related pathways were re-explored in cell models with SH2D1A overexpression using the CCK-8, migration and invasion assays and western blotting. The functions and mechanisms of genes co-expressed with SH2D1A were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The relationship between SH2D1A expression and immune microenvironment features in HCC was explored.

RESULTS

Elevated SH2D1A expression promoted cell proliferation, migration, and invasion, which was related to the overexpression of p-Nf-κB and BCL2A1 protein levels in HCC. SH2D1A expression was related to the immune, stromal, and ESTIMATE scores, and the abundance of immune cells, such as B cells, CD8+ T cells, and T cells. SH2D1A expression was significantly related to the expression of immune cell markers, such as PDCD1, CD8A, and CTLA4 in HCC.

CONCLUSION

SH2D1A overexpression was found to promote cell growth and metastasis via the Nf-κB signaling pathway and may be related to the immune microenvironment in HCC. The findings indicate that SH2D1A can function as a biomarker in HCC.

Weighted gene co-expression network reveals driver genes contributing to phenotypes of anaplastic thyroid carcinoma and immune checkpoint identification for therapeutic targets

Background

Anaplastic thyroid carcinoma (ATC) is a rare but extremely malignant tumor, with a rapid growth rate and early metastasis thus leading to poor survival of patients. The molecular mechanisms underlying these aggressive traits of ATC remain unknown, which impedes the substantial progress in treatment to prolong ATC patient survival.

Methods

We applied weighted gene co-expression network analysis (WGCNA) to identify ATC-specific modules. The Metascape web and R package clusterProfiler were employed to perform enrichment analysis. Combined with differentially expressed gene analysis, we screened out the most potential driver genes and validated them using receiver operator characteristic (ROC) analysis, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blotting, immunohistochemistry (IHC), and triple immunofluorescence staining.

Results

A gene expression matrix covering 75 normal samples, 83 papillary thyroid carcinoma (PTC), 26 follicular thyroid carcinoma (FTC), 19 poor-differentiated thyroid carcinoma (PDTC), and 41 ATC tissue samples were integrated, based on which we detected three most potential ATC-specific modules and found that hub genes of these modules were enriched in distinct biological signals. Hub genes in the turquoise module were mainly enriched in mitotic cell cycle, tube morphogenesis, and cell differentiation, hub genes in the magenta module were mainly clustered in the extracellular matrix organization, positive regulation of cell motility, and regulation of Wnt signaling pathway, while hub genes in the blue module primarily participated in the inflammatory response, innate immune response, and adaptive immune response. We showed that 9 top genes, 8 transcription factors (TFs), and 4 immune checkpoint genes (ICGs) were differentially expressed in ATC compared to other thyroid samples and had high diagnostic values for ATC, among which, 9 novel ATC-specific genes (ADAM12, RNASE2, CASP5, KIAA1524, E2F7, MYBL1, SRPX2, HAVCR2, and TDO2) were validated with our clinical samples. Furthermore, we illustrated that ADAM12, RNASE2, and HAVCR2 were predominantly present in the cytoplasm.

Conclusion

Our study identified a set of novel ATC-specific genes that were mainly related to cell proliferation, invasion, metastasis, and immunosuppression, which might throw light on molecular mechanisms underlying aggressive phenotypes of ATC and provide promisingly diagnostic biomarkers and therapeutic targets.

Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis

Aim

Methods

Results

Conclusions

Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

Background

The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT.

Methods

Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database.

Results

In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT.

Conclusions

These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02409-4.

Comprehensive characterization of pathological stage-related genes of papillary thyroid cancer along with survival prediction

It is crucial to understand the differences across papillary thyroid cancer (PTC) stages, so as to provide a basis for individualized treatments. Here, comprehensive function characterization of PTC stage‐related genes was performed and a new prognostic signature was developed for advanced patients. Two gene modules were confirmed to be closely associated with PTC stages and further six hub genes were identified that yield excellent diagnostic efficiency between tumour and normal tissues. Genetic alteration analysis indicates that they are much conservative since mutations in the DNA of them rarely occur, but changes of DNA methylation on these six genes show that 12 DNA methylation sites are significantly associated with their corresponding genes' expression. Validation data set testing also suggests that these six stage‐related hub genes would be probably potential biomarkers for marking four stages. Subsequently, a 21‐mRNA‐based prognostic risk model was constructed for PTC stage III/IV patients and it could effectively predict the survival of patients with strong prognostic ability. Functional analysis shows that differential expression genes between high‐ and low‐risk patients would promote the progress of PTC to some extent. Moreover, tumour microenvironment (TME) of high‐risk patients may be more conducive to tumour growth by ESTIMATE analysis.

Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles

Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein-protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.

Identification of key genes in hepatitis B associated hepatocellular carcinoma based on WGCNA

Chronic Infection of Hepatitis B virus (HBV) is one risk factor of hepatocellular carcinoma (HCC). Much effort has been made to research the process of HBV-associated HCC, but its molecular mechanisms of carcinogenesis remain vague. Here, weighted gene co-expression network analysis (WGCNA) was employed to explore the co-expressed modules and hub/key genes correlated to HBV-associated HCC. We found that genes of the most significant module related to HBV-associated HCC were enriched in DNA replication, p53 signaling pathway, cell cycle, and HTLV-1 infection associated pathway; these cellular pathways played critical roles in the initiation and development of HCC or viral infections. Furthermore, seven hub/key genes were identified based on the topological network analysis, and their roles in HCC were verified by expression and Kaplan-Meier survival analysis. Protein-protein interaction and KEGG pathway analysis suggested that these key genes may stimulate cellular proliferation to promote the HCC progression. This study provides new perspectives to the knowledge of the key pathways and genes in the carcinogenesis process of HBV-associated HCC, and our findings provided potential therapeutic targets and clues of the carcinogenesis of HBV-associated HCC.

Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysis

Glioma is the most common brain tumor with high mortality. However, there are still challenges for the timely and accurate diagnosis and effective treatment of the tumor. One hundred and twenty-one samples with grades II, III and IV from the Gene Expression Omnibus database were used to construct gene co-expression networks to identify hub modules closely related to glioma grade, and performed pathway enrichment analysis on genes from significant modules. In gene co-expression network constructed by 2345 differentially expressed genes from 121 gene expression profiles for glioma, we identified the black and blue modules that associated with grading. The module preservation analysis based on 118 samples indicates that the two modules were replicable. Enrichment analysis showed that the extracellular matrix genes were enriched for blue module, while cell division genes were enriched for black module. According to survival analysis, 21 hub genes were significantly up-regulated and one gene was significantly down-regulated. What’s more, IKBIP, SEC24D, and FAM46A are the genes with little attention among the 22 hub genes. In this study, IKBIP, SEC24D, and FAM46A related to glioma were mentioned for the first time to the current knowledge, which might provide a new idea for us to study the disease in the future. IKBIP, SEC24D and FAM46A among the 22 hub genes identified that are related to the malignancy degree of glioma might be used as new biomarkers to improve the diagnosis, treatment and prognosis of glioma.

miR-133b Suppresses Invasion and Migration of Gastric Cancer Cells via the COL1A1/TGF-β Axis

Objective

The study aimed to explore the mechanism of miR-133b regulating the invasion and migration of gastric cancer (GC) cells via the COL1A1/TGF-β axis.

Methods

The miRNA expression profiles of GC downloaded from TCGA database were subjected to differential analysis to determine the target miRNA of interest, and the target genes of the miRNA were predicted by bioinformatics. GSEA was used for gene enrichment analysis. qRT-PCR was carried out to detect gene expression in GC cells. The effect of miR-133b on GC cells was examined by CCK-8, wound healing and Transwell assays. Western blot was conducted to assess the protein expression of EMT-related proteins. The binding relationship between genes was verified by dual-luciferase reporter gene assay.

Results

The expression of miR-133b was markedly downregulated in GC tissue, while that of COL1A1 was upregulated. Overexpression of miR-133b decreased the migration and invasion of GC cells, and the EMT process was inhibited as well, while inverse results were observed when miR-133b was silenced. COL1A1 was a target gene of miR-133b and its overexpression had a significant impact on the prognosis of patients. GSEA pathway enrichment results showed that COL1A1 was markedly enriched in the TGF-β signaling pathway. In addition, COL1A1 overexpression induced the activation of the TGF-β signaling pathway to promote proliferation and migration of GC cells, whereas miR-133b overexpression suppressed the signaling pathway. Thus, overexpression of miR-133b and COL1A1 simultaneously would reverse the inhibitory effect of miR-133b on cell invasion and migration.

Conclusion

In this study, miR-133b was found to inhibit the invasion and migration of GC cells via the COL1A1/TGF-β axis, which provides a new research direction for the diagnosis and targeted therapy of GC.

Nine Genes Mediate the Therapeutic Effects of Iodine-131 Radiotherapy in Thyroid Carcinoma Patients

Background

Thyroid carcinoma (THCA) is one of the most common malignancies of the endocrine system, which is usually treated by surgery combined with iodine-131 (I¹³¹) radiotherapy.

Aims

This study is aimed at exploring the potential targets of I¹³¹ radiotherapy in THCA.

Methods

The RNA-sequencing data of THCA in The Cancer Genome Atlas database (including 568 THCA samples) was downloaded. The differentially expressed genes (DEGs) between the tumour samples whether or not subjected to I¹³¹ radiotherapy were identified using edgeR package. Using the WGCNA package, the module that was relevant with I¹³¹ radiotherapy was selected. The intersection genes of the hub module nodes and the DEGs were obtained as hub genes, followed by the function and pathway enrichment analyses using the clusterProfiler package. Moreover, the protein-protein interaction (PPI) network for the hub genes was constructed using Cytoscape software. In addition, more important hub genes were analysed with function mining using the GenCLiP2 online tool. The qPCR analysis was used to verify the mRNA expression of more important hub genes in THCA tissues.

Results

There were 500 DEGs (167 upregulated and 333 downregulated) between the two groups. WGCNA analysis showed that the green module (428 nodes) exhibited the most significant correlation with I¹³¹ radiotherapy. A PPI network was built after the identification of 53 hub genes. In the PPI network, CDH5, KDR, CD34, FLT4, EMCN, FLT1, ROBO4, PTPRB, and CD93 exhibited higher degrees, which were mainly implicated in the vascular function. The relative expression of nine mRNAs in the THCA tissues treated with I¹³¹ was lower.

Conclusion

I¹³¹ radiotherapy might exert therapeutic effects by targeting CDH5, KDR, CD34, FLT4, EMCN, FLT1, ROBO4, PTPRB, and CD93 in THCA patients.

Co-expression based cancer staging and application

A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample. The R source code used in this study is available at GitHub (https://github.com/yxchspring/CECS).

Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis

Lung cancer is one of the most malignant tumors in the world. Early diagnosis and treatment of lung cancer are vitally important to reduce the mortality of lung cancer patients. In the present study, we attempt to identify the candidate biomarkers for lung cancer by weighted gene co-expression network analysis (WGCNA). Gene expression profile of GSE30219 was downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the limma package, and the co-expression modules of genes were built by WGCNA. UALCAN was used to analyze the relative expression of normal group and tumor subgroups based on tumor individual cancer stages. Survival analysis for the hub genes was performed by Kaplan–Meier plotter analysis with the TCGA database. A total of 2176 genes (745 upregulated and 1431 downregulated genes) were obtained from the GSE30219 database. Seven gene co-expression modules were conducted by WGCNA and the blue module might be inferred as the most crucial module in the pathogenesis of lung cancer. In the pathway enrichment analysis of KEGG, the candidate genes were enriched in the “DNA replication,” “Cell cycle,” and “P53 signaling pathway” pathways. Among these, the cell cycle pathway was the most significant pathway in the blue module with four hub genes CCNB1, CCNE2, MCM7, and PCNA which were selected in our study. Kaplan–Meier plotter analysis indicated that the high expressions of four hub genes were correlated with a worse overall survival (OS) and advanced tumors. qRT-PCR showed that mRNA expression levels of MCM7 (p = 0.038) and CCNE2 (0.003) were significantly higher in patients with the TNM stage. In summary, the high expression of the MCM7 and CCNE2 were significantly related with advanced tumors and worse OS in lung cancer. Thus, the MCM7 and CCNE2 genes can be good indicators for cellular proliferation and prognosis in lung cancer.