Lushun Yuan

Downregulation of TRPM7 suppressed migration and invasion by regulating epithelial–mesenchymal transition in prostate cancer cells

Metastasis is a leading cause of death in patients with prostate cancer (PCa). Transient receptor potential channel 7 (TRPM7) functions as a Mg2+/Ca2+-permeable channel as well as a protein kinase that regulate various cellular processes including cell adhesion, migration and survival. However, the function of TRPM7 in metastasis of PCa remains largely unknown. Microarray analysis suggested that calcium signaling pathway was significantly altered in metastatic PCa tissues, compared with benign prostatic hyperplasia tissues. Bioinformatics analysis using microarray data and database for annotation, visualization and integrated discovery database revealed altered genes involved in calcium signaling pathway were significantly upregulated in TRPM7 deficiency PCa cells, which was also confirmed by experimental verification. Therefore, we aim to investigate the role of TRPM7 in human PCa cell migration and invasion as well as the underlying mechanisms. We observed that TRPM7 was upregulated in PCa cells and tissues compared with prostate hyperplasia cells and tissues. Further investigations suggested that TRPM7 deficiency suppressed migration and invasion of distinct PCa cell lines while overexpression of TRPM7 increased migration of PCa cells. In addition, knockdown of TRPM7 in PCa cells reversed the epithelial–mesenchymal transition (EMT) status, accompanied by downregulation of MMPs and upregulation of E-cadherin. Taken together, our study indicated that downregulation of TRPM7 could inhibit migration and invasion via reversing EMT status in PCa cells.

Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In current study, the microarray data GSE66272 containing ccRCC and adjacent normal tissues was analyzed to identify 4042 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 12 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = -0.77) by Pearsons correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on inflammatory response, immune response, chemotaxis (all p < 1e-10). In the significant module, a total of 38 network hub genes were identified, FCER1G exhibited the highest correlation (r = 0.95) with ccRCC progression. In addition, FCER1G was hub node in the protein-protein interaction network of the genes in blue module as well. Thus, FCER1G was subsequently selected for validation. In the test set GSE53757 and RNA-sequencing data, FCER1G expression was also positively correlated with four stages of ccRCC progression (p < 0.001). Receiver operating characteristic (ROC) curve indicated that FCER1G could distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) ccRCC (AUC=0.74, p < 0.001). Besides, FCER1G could be a prognostic gene in clinical practice as well, revealed by survival analysis based on RNA-sequencing data (p < 0.05). In conclusion, using weighted gene co-expression analysis, FCER1G was identified and validated in association with ccRCC progression and prognosis, which might improve the prognosis by influencing immune-related pathways.

Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis

Human clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, we screened differential expressed genes, and constructed protein-protein interaction (PPI) network and a weighted gene co-expression network to identify key genes and pathways associated with the progression of ccRCC (n = 56). Functional and pathway enrichment analysis demonstrated that upregulated differentially expressed genes (DEGs) were significantly enriched in response to wounding, positive regulation of immune system process, leukocyte activation, immune response and cell activation. Downregulated DEGs were significantly enriched in oxidation reduction, monovalent inorganic cation transport, ion transport, excretion and anion transport. In the PPI network, top 10 hub genes were identified (TOP2A, MYC, ALB, CDK1, VEGFA, MMP9, PTPRC, CASR, EGFR and PTGS2). In co-expression network, 6 ccRCC-related modules were identified. They were associated with immune response, metabolic process, cell cycle regulation, angiogenesis and ion transport. In conclusion, our study illustrated the hub genes and pathways involved in the progress of ccRCC, and further molecular biological experiments are needed to confirm the function of the candidate biomarkers in human ccRCC.

Bioinformatics and functional analyses of key genes and pathways in human clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. The present study was conducted to explore the mechanisms and identify the potential target genes for ccRCC using bioinformatics analysis. The microarray data of GSE15641 were screened on Gene-Cloud of Biotechnology Information (GCBI). A total of 32 ccRCC samples and 23 normal kidney samples were used to identify differentially expressed genes (DEGs) between them. Subsequently, the clustering analysis and functional enrichment analysis of these DEGs were performed, followed by protein-protein interaction (PPI) network, and pathway relation network. Additionally, the most significant module based on PPI network was selected, and the genes in the module were identified as hub genes. Furthermore, transcriptional level, translational level and survival analyses of hub genes were performed to verify the results. A total of 805 genes, 403 upregulated and 402 downregulated, were differentially expressed in ccRCC samples compared with normal controls. The subsequent bioinformatics analysis indicated that the small molecule metabolic process and the metabolic pathway were significantly enriched. A total of 7 genes, including membrane metallo-endopeptidase (MME), albumin (ALB), cadherin 1 (CDH1), prominin 1 (ROM1), chemokine (C-X-C motif) ligand 12 (CXCL12), protein tyrosine phosphatase receptor type C (PTPRC) and intercellular adhesion molecule 1 (ICAM1) were identified as hub genes. In brief, the present study indicated that these candidate genes and pathways may aid in deciphering the molecular mechanisms underlying the development of ccRCC, and may be used as therapeutic targets and diagnostic biomarkers of ccRCC.

Overexpression of COL3A1 confers a poor prognosis in human bladder cancer identified by co-expression analysis

Human bladder cancer (BCa) is one of the worldwide cancers in men and women populations, with the etiology and mechanism unknown. In our study, we constructed a weighted gene co-expression network to identify gene modules associated with the progression of BCa (n = 93). In the significant module (R2 = 0.48), a total of 103 network hub genes were identified, and 4 of them were hub nodes in the protein-protein interaction network as well. In validation, COL3A1 showed a higher correlation with the disease progression than any other hub genes in hub module in the test set (p < 0.001). Functional and pathway enrichment analysis demonstrated that COL3A1 is overrepresented in pathway of focal adhesion, which associated with tumor progression and might cause metastasis. Gene set enrichment analysis (GSEA) also demonstrated that the gene set of “MAPK signaling pathway” and focal adhesion related pathways were enriched in BCa samples with COL3A1 highly expressed (FDR < 0.05). Considering the clinicopathological parameters, highly-expressed COL3A1 was closely correlated with local recurrence and BCa stage. Survival analysis revealed that BCa patients with higher expression of COL3A1 had a significantly shorter overall survival time and disease free survival time.In conclusion, based on the co-expression analysis, COL3A1 was identified in the association with progression and prognosis of BCa, which might refer a poor prognosisprobably by regulating MAPK signaling pathway.

A novel correlation between ATP5A1 gene expression and progression of human clear cell renal cell carcinoma identified by co‑expression analysis

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidneys, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, a weighted gene co-expression network was constructed to identify gene modules associated with the progression of ccRCC (n=35). In the significant module (R2 = −0.53), a total of 13 network hub genes were identified, and 2 of them were hub nodes in the protein-protein interaction network as well. In validation, ATP5A1 showed a higher correlation with the disease progression than any other hub gene in the hub module (P=0.001219). In the test set (n=202), ATP5A1 was also highly expressed in normal kidney than ccRCC tissues of each grade (P<0.001). Functional and pathway enrichment analysis demonstrated that ATP5A1 is overrepresented in pathway of oxidative phosphorylation, which associated with tumorigenesis and tumor progression. Gene set enrichment analysis (GSEA) also demonstrated that the gene set of ‘oxidative phosphorylation’ and metabolic pathways were enriched in ccRCC samples with ATP5A1 highly expressed (P<0.05). In conclusion, based on the co-expression analysis, ATP5A1 was validated to be associated with progression of ccRCC, probably by regulating tumor-related phosphorylation.

Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

Human clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumors. We constructed a weighted gene co-expression network to identify gene modules associated with clinical features of ccRCC (n = 97). Six hub genes (CCNB2, CDC20, CEP55, KIF20A, TOP2A and UBE2C) were identified in both co-expression and protein-protein interaction (PPI) networks, which were highly correlated with pathologic stage. The significance of expression of the hub genes in ccRCC was ranked top 4 among all cancers and correlated with poor prognosis. Functional analysis revealed that the hub genes were significantly enriched in cell cycle regulation and cell division. Gene set enrichment analysis suggested that the samples with highly expressed hub gene were correlated with cell cycle and p53 signaling pathway. Taken together, six hub genes were identified to be associated with progression and prognosis of ccRCC, and they might lead to poor prognosis by regulating p53 signaling pathway.

Identifying a Novel Biomarker TOP2A of Clear Cell Renal Cell Carcinoma (ccRCC) Associated with Smoking by Co-Expression Network Analysis

Although it is well known that smoking is one of pathogenesis of clear cell renal cell carcinoma (ccRCC), the underlying molecular mechanism is still unclear. In our study, the microarray dataset GSE46699 is analyzed by weighted gene co-expression network analysis (WGCNA). Then we identify 15 co-expressed gene modules in which the lightcyan module (R2 = 0.30) is the most significant. Combined with the protein-protein interaction (PPI) network and WGCNA, two hub genes are identified. Meanwhile, linear regression analyses indicate that TOP2A has a higher connection with smoking in ccRCC, survival analysis proved that overexpression of TOP2A in ccRCC could lead to shorter survival time. Furthermore, bioinformatical analyses based on GSE46699 and GSE2109 as well as qRT-PCR experiment show similar results that TOP2A is significantly up-regulated in smoking ccRCC compared to non-smoking ccRCC samples. In addition, Functional analysis, pathway enrichment analysis and gene set enrichment analysis (GSEA) indicate that high expression of TOP2A is related to cell cycle and p53 signaling pathway in ccRCC samples. Moreover, in vitro experiments revealed that TOP2A induced cell cycle arrest at G2 phase and proliferation inhibition via p53 phosphorylation. Taken together, by using WGCNA, we have identified a novel biomarker named TOP2A, which could affect the development of smoking-related ccRCC by regulating cell cycle and p53 signaling pathway.

Identification of Biomarkers Associated With Pathological Stage and Prognosis of Clear Cell Renal Cell Carcinoma by Co-expression Network Analysis

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer whose prognostic is affected by the tumor progression associated with complex gene interactions. However, there is currently no available molecular markers associated with ccRCC progression and used or clinical application. In our study, microarray data of 101 ccRCC samples and 95 normal kidney samples were analyzed and 2,425 differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis (WGCNA) was then conducted and 11 co-expressed gene modules were identified. Module preservation analysis revealed that two modules (red and black) were found to be most stable. In addition, Pearsons correlation analysis identified the module most relevant to pathological stage(patho-module) (r = 0.44, p = 3e-07). Functional enrichment analysis showed that biological processes of the patho-module focused on cell cycle and cell division related biological process and pathway. In addition, 29 network hub genes highly related to ccRCC progression were identified from the stage module. These 29 hub genes were subsequently validated using 2 other independent datasets including GSE53757 (n = 72) and TCGA (n = 530), and the results indicated that all hub genes were significantly positive correlated with the 4 stages of ccRCC progression. Kaplan-Meier survival curve showed that patients with higher expression of each hub gene had significantly lower overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence/progression biomarkers of ccRCC. In summary, we identified 29 molecular markers correlated with different pathological stages of ccRCC. They may have important clinical implications for improving risk stratification, therapeutic decision and prognosis prediction in ccRCC patients.

Co-expression Network Analysis of Biomarkers for Adrenocortical Carcinoma

Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. And currently, there are no specific diagnostic biomarkers for ACC. In our study, we aimed to screen biomarkers for disease diagnosis, progression and prognosis. We firstly used the microarray data from public database Gene Expression Omnibus database to construct a weighted gene co-expression network, and then to identify gene modules associated with clinical features of ACC. Though this algorithm, a significant module with R2 = 0.64 (P = 9 × 10-5) was identified. Co-expression network and protein–protein interaction network were performed for screen the candidate hub genes. Checked by The Cancer Genome Atlas (TCGA) database, another independent dataset GSE19750, and GEPIA database, using one-way ANOVA, Pearson’s correlation, survival analysis, diagnostic capacity (ROC curve) and expression level revalidation, a total 12 real hub genes were identified. Gene ontology and KEGG pathway analysis of genes in the significant module revealed that the hub genes are significantly enriched in cell cycle regulation. Moreover, gene set enrichment analysis suggests that the samples with highly expressed hub genes are correlated with cell cycle. Taken together, our integrated analysis has identified 12 hub genes that are associated with the progression and prognosis of ACC; these hub genes might lead to poor outcomes by regulating the cell cycle.