Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA

Analysis of lncRNA-mRNA networks after MEK1/2 inhibition based on WGCNA in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDA) responds poorly to treatment. Efforts have been exerted to prolong the survival time of PDA, but the 5-year survival rates remain disappointing. Understanding the molecular mechanisms of PDA development is significant. MEK/ERK pathway signaling has been proven to be important in PDA. lncRNA-mRNA networks have become a vital part of molecular mechanisms in the MEK/ERK pathway. Herein, weighted gene coexpression network analysis was used to investigate the coexpressed lncRNA-mRNA networks in the MEK/ERK pathway based on GSE45765. Differently expressed long noncoding RNA (lncRNA) and messenger RNA (mRNA) were found and 10 modules were identified based on coexpression profiles. Gene ontology and Kyoto Encyclopedia of Genes and Genomes were then performed to analyze the coexpressed lncRNA and mRNA in different modules. PDA cells and tissues were used to validate the analysis results. Finally, we found that NONHSAT185150.1 and B4GALT6 were negatively correlated with MEK1/2. By analyzing GSE45765, the genome-wide profiles of lncRNA-mRNA network after MEK1/2 was established, which might aid the development of drug-targeting MEK1/2 and the investigation of diagnostic markers.

Identification of Genes and Pathways Associated with Acne Using Integrated Bioinformatics Methods

Background: Acne is the most common skin inflammatory condition. The pathogenesis of acne is not fully understood. Aims: We performed weighted gene co-expression network analysis (WGCNA) to select acne-associated genes and pathways. Methods: GSE53795 and GSE6475 datasets including data from lesional and nonlesional skin of acne patients were downloaded from the NCBI Gene Expression Omnibus. Differentially expressed genes (DEGs) in lesions were identified following a false discovery rate <0.05 and | log2 fold change | ≥0.5. DEG-associated biological processes and pathways were identified. WGCNA analysis was performed to identify acne-associated modules. DEGs in the acne-associated modules were used for protein-protein interaction (PPI) network construction and Gene Set Enrichment Analysis (GSEA). Acne-associated candidate DEGs and pathways were identified together with items in the Comparative Toxicogenomics Database (CTD). Results: A total of 2,140 and 1,190 DEGs were identified in GSE53795 and GSE6475 datasets, respectively, including 716 overlapping DEGs with similar expression profiles in the two datasets, which were clustered into 10 consensus modules. Two modules (brown and turquoise, 359 genes) were associated with acne phenotype. Of these 359 DEGs, 254 were enrolled in the PPI network. GSEA showed that these DEGs were associated with chemokine signaling pathway, cytokine-cytokine receptor interaction, and natural killer cell-mediated cytotoxicity. After identification in CTD, one pathway Cytokine-cytokine receptor interaction and 24 acne-associated DEGs, including IL1R1, CXCL1, CXCR4, CCR1, CXCL2 and IL1β, were identified as candidates associated with acne. Conclusion: Our results highlight the important roles of the proinflammatory cytokines including IL1β, CXCL1, CXCL2, CXCR4, and CCR1 in acne pathogenesis or therapeutic management.

Identification of Hub Genes and Key Pathways Associated With Bipolar Disorder Based on Weighted Gene Co-expression Network Analysis

Bipolar disorder (BD) is a complex mental disorder with high mortality and disability rates worldwide; however, research on its pathogenesis and diagnostic methods remains limited. This study aimed to elucidate potential candidate hub genes and key pathways related to BD in a pre-frontal cortex sample. Raw gene expression profile files of GSE53987, including 36 samples, were obtained from the gene expression omnibus (GEO) database. After data pre-processing, 10,094 genes were selected for weighted gene co-expression network analysis (WGCNA). After dividing highly related genes into 19 modules, we found that the pink, midnight blue, and brown modules were highly correlated with BD. Functional annotation and pathway enrichment analysis for modules, which indicated some key pathways, were conducted based on the Enrichr database. One of the most remarkable significant pathways is the Hippo signaling pathway and its positive transcriptional regulation. Finally, 30 hub genes were identified in three modules. Hub genes with a high degree of connectivity in the PPI network are significantly enriched in positive regulation of transcription. In addition, the hub genes were validated based on another dataset (GSE12649). Taken together, the identification of these 30 hub genes and enrichment pathways might have important clinical implications for BD treatment and diagnosis.

Prognostic genes of hepatocellular carcinoma based on gene coexpression network analysis

Hepatocellular carcinoma (HCC) is the most common subtype in liver cancer whose prognosis is affected by malignant progression associated with complex gene interactions. However, there is currently no available biomarkers associated with HCC progression in clinical application. In our study, RNA sequencing expression data of 50 normal samples and 374 tumor samples was analyzed and 9225 differentially expressed genes were screened. Weighted gene coexpression network analysis was then conducted and the blue module we were interested was identified by calculating the correlations between 17 gene modules and clinical features. In the blue module, the calculation of topological overlap was applied to select the top 30 genes and these 30 genes were divided into the green group (11 genes) and the yellow group (19 genes) through searching whether these genes were validated by in vitro or in vivo experiments. The genes in the green group which had never been validated by any experiments were recognized as hub genes. These hub genes were subsequently validated by a new data set GSE76427 and KM Plotter Online Tool, and the results indicated that 10 genes (FBXO43, ARHGEF39, MXD3, VIPR1, DNASE1L3, PHLDA1, CSRNP1, ADR2B, C1RL, and CDC37L1) could act as prognosis and progression biomarkers of HCC. In summary, 10 genes who have never been mentioned in HCC were identified to be associated with malignant progression and prognosis of patients. These findings may contribute to the improvement of the therapeutic decision, risk stratification, and prognosis prediction for HCC patients.

Integrated bioinformatics analysis to identify 15 hub genes in breast cancer

The aim of the present study was to identify the hub genes and provide insight into the tumorigenesis and development of breast cancer. To examine the hub genes in breast cancer, integrated bioinformatics analysis was performed. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the ‘limma’ package in R. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis was used to determine the functional annotations and potential pathways of the DEGs. Subsequently, a protein-protein interaction network analysis and weighted correlation network analysis (WGCNA) were conducted to identify hub genes. To confirm the reliability of the identified hub genes, RNA gene expression profiles were obtained from The Cancer Genome Atlas (TCGA)-breast cancer database, and WGCNA was used to screen for genes that were markedly correlated with breast cancer. By combining the results from the GEO and TCGA datasets, 15 hub genes were identified to be associated with breast cancer pathophysiology. Overall survival analysis was performed to examine the association between the expression of hub genes and the overall survival time of patients with breast cancer. Higher expression of all hub genes was associated with significantly shorter overall survival in patients with breast cancer compared with patients with lower levels of expression of the respective gene.

Eleven genes associated with progression and prognosis of endometrial cancer (EC) identified by comprehensive bioinformatics analysis

Background

Endometrial cancer (EC) is one of the female malignant tumors. Endometrial cancer predominately affects post-menopausal women. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression.

Methods

Download the gene expression profile from Gene Expression Omnibus (GEO). Calculate raw expression data according to pre-processing procedures. We performed the “limma” R language package to screen DEGs between Endometrial cancer tissue samples and normal uterus tissue samples. Enrichment of the functions and pathways was analyzed by using clusterprofiler. We utilized Search Tool for the Retrieval of Interacting Genes Database (STRING) to assess protein–protein interaction (PPI) information, and then we used plug-in Molecular Complex Detection (MCODE) to screen hub modules of PPI network in Cytoscape. We also performed functional analysis on the genes in the hub module by using clusterprofiler. Next, we utilized the “WGCNA” package in R to establish co-expression network for the DEGs. The Venn diagram was performed to overlap the gene in key module and hub PPI cluster. We validated the key genes in TCGA, GEPIA, UALCAN and Immunohistochemistry staining obtained from The Human Protein Atlas database. And then we did ROC curve analysis by SPSS. Gene set enrichment analysis (GSEA) and mutation analysis were also performed for hub genes.

Results

Functional and pathway enrichment analysis demonstrated that the upregulated differentially expressed genes (DEGs) were significantly enriched in CXCR chemokine receptor binding, chemokine activity, chemokine receptor binding, G-protein coupled receptor binding, RAGE receptor binding, cytokine activity, microtubule binding, receptor regulator activity and microtubule motor activity, and the down-regulated genes were highly enriched in collagen binding. After using STRING software to construct PPI network, 30 prominent proteins were identified and the first two significant modules were selected. In co-expression network, 5 EC-related modules were identified. Among them, the turquoise module has the highest correlation with the EC. We further analyzed the genes in the PPI and turquoise module, and selected eleven key genes related to EC after validation of TCGA database, GEPIA, UALCAN and immunohistochemistry. Six of them had mutation significance.

Conclusions

In summary, these 11 genes may become new therapy targets for EC treatment.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0859-1) contains supplementary material, which is available to authorized users.

Developmental transcriptomics of Chinese cordyceps reveals gene regulatory network and expression profiles of sexual development-related genes

BACKGROUND

Chinese cordyceps, also known as Chinese caterpillar fungus (Ophiocordyceps sinensis, syn. Cordyceps sinensis), is of particular interest for its cryptic life cycle and economic and ecological importance. The large-scale artificial cultivation was succeeded recently after several decades of efforts and attempts. However, the induction of primordium, sexual development of O. sinensis and the molecular basis of its lifestyle still remain cryptic.

RESULTS

The developmental transcriptomes were analyzed for six stages covering the whole developmental process, including hyphae (HY), sclerotium (ST), primordium (PR), young fruiting body (YF), developed fruiting body (DF) and mature fruiting body (MF), with a focus on the expression of sexual development-related genes. Principal component analysis revealed that the gene expression profiles at the stages of primordium formation and fruiting body development are more similar than those of the undifferentiated HY stage. The PR and MF stages grouped together, suggesting that primordium differentiation and sexual maturation have similar expression patterns. Many more DEGs were identified between the ST and HY stages, covering 47.5% of the O. sinensis genome, followed by the comparisons between the ST and PR stages. Using pairwise comparisons and weighted gene coexpression network analysis, modules of coexpressed genes and candidate hub genes for each developmental stage were identified. The four mating type loci genes expressed during primordium differentiation and sexual maturation; however, spatiotemporal specificity of gene expression indicated that they also expressed during the anamorphic HY stage. The four mating type genes were not coordinately expressed, suggesting they may have divergent roles. The expression of the four mating type genes was highest in the fertile part and lowest in the sclerotium of the MF stage, indicating that there is tissue specificity. Half of genes related to mating signaling showed as the highest expression in the ST stage, indicating fruiting was initiated in the ST stage.

CONCLUSIONS

These results provide a new perspective to understanding of the key pathways and hub genes, and sexual development-related gene profile in the development of Chinese cordyceps. It will be helpful for underlying sexual reproduction, and add new information to existing models of fruiting body development in edible fungi.

Identification of gene coexpression modules, hub genes, and pathways related to spinal cord injury using integrated bioinformatics methods

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway "natural killer cell-mediated cytotoxicity" were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the "natural killer cell-mediated cytotoxicity" pathway.

Brassica napus Infected with Leptosphaeria maculans

Alternative splicing (AS) is a post-transcriptional regulatory process that enhances transcriptome diversity, thereby affecting plant growth, development, and stress responses. To identify the new transcripts and changes in the isoform-level AS landscape of rapeseed (Brassica napus) infected with the fungal pathogen Leptosphaeria maculans, we compared eight RNA-seq libraries prepared from mock-inoculated and inoculated B. napus cotyledons and stems. The AS events that occurred in stems were almost the same as those in cotyledons, with intron retention representing the most common AS pattern. We identified 1892 differentially spliced genes between inoculated and uninoculated plants. We performed a weighted gene co-expression network analysis (WGCNA) to identify eight co-expression modules and their Hub genes, which are the genes most connected with other genes within each module. There are nine Hub genes, encoding nine transcription factors, which represent key regulators of each module, including members of the NAC, WRKY, TRAF, AP2/ERF-ERF, C2H2, C2C2-GATA, HMG, bHLH, and C2C2-CO-like families. Finally, 52 and 117 alternatively spliced genes in cotyledons and stems were also differentially expressed between mock-infected and infected materials, such as HMG and C2C2-Dof; which have dual regulatory mechanisms in response to L. maculans. The splicing of the candidate genes identified in this study could be exploited to improve resistance to L. maculans.

Weighted gene coexpression network analysis identifies specific transcriptional modules and hub genes related to intramuscular fat traits in chicken breast muscle

Intramuscular fat (IMF) traits are important factors that influence meat quality. However, the molecular regulatory mechanisms that underlie this trait in chickens are still poorly understood at the gene coexpression level. Here, we performed a weighted gene coexpression network analysis between IMF traits and transcriptome profile in breast muscle in the Chinese domestic Gushi chicken breed at 6, 14, 22, and 30 weeks. A total of 26 coexpressed gene modules were identified. Six modules, which included the dark gray, purple, cyan, pink, light cyan, and blue modules, showed a significant positive correlation (P < 0.05) with IMF traits. The strongest correlation was observed between the dark gray module and IMF content (r = 0.85; P = 4e-04) and between the blue module and different fatty acid content (r = 0.87~0.91; P = 5e-05~2e-04). Enrichment analysis showed that the enrichment of biological processes, such as fatty acid metabolic process, fat cell differentiation, acylglycerol metabolic process, and glycerolipid metabolism were significantly different in the six modules. In addition, the 32, 24, 4, 7, 6, and 25 hub genes were identified from the blue, pink, light cyan, cyan, dark gray, and purple modules, respectively. These hub genes are involved in multiple links to fatty acid metabolism, phospholipid metabolism, cholesterol metabolism, diverse cellular behaviors, and cell events. These results provide novel insights into the molecular regulatory mechanisms for IMF-related traits in chicken and may also help to uncover the formation mechanism for excellent meat quality traits in local breeds of Chinese chicken.

ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer

Background

Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable.

Materials and methods

Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples.

Results

We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA.

Conclusion

In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.

TIGD1, a gene of unknown function, involves cell-cycle progression and correlates with poor prognosis in human cancer

BACKGROUND

Trigger transposable element-derived 1 (TIGD1), a human species-specific gene, was identified as a cell cycle-related differentially expressed gene during the formation of liver cancer. As far as we know, there are no reports about the function of TIGD1. This study aimed to explore the expression of TIGD1 in human cancers and establish whether elevated TIGD1 can be used as a prognostic cancer biomarker and its potential role in cancer.

METHODS

Molecular profiling of TIGD1 in human cancers was assessed using a series of databases, including Oncomine, COSMIC, Kaplan-Meier, UCSC, and cBioPortal.

RESULTS

We found that TIGD1 overexpressed in colorectal, gastric, liver, lung, and pancreatic cancers than their normal tissues and its expression might be negatively related with the prognosis. The TIGD1 coexpression genes were obtained from cBioPortal and analyzed using ClueGO plugin in Cytoscape to predict the function of TIGD1.

CONCLUSIONS

In summary, the elevated TIGD1 expression is coupled with a malignant survival rate in several cancers. It may play its role by regulating cell-cycle progression. These findings provide fresh insight into our understanding of TEs in cancers. As a previously unreported gene, future research is required to validate our findings and illuminate the molecular mechanisms. In conclusion, we systemically analyze the expression, prognostic value, and likely role of TIGD1, the unknown function gene, in cancer. Our finding provides evidence that TIGD1 involve in the cell cycle. These findings provide fresh insight into our understanding of TEs in cancers. Next, the detailed mechanism of TIGD1 needs to be studied in the future.

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.