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Renal cell carcinoma (RCC), as one of the most common urological malignancies, has many histologic and molecular subtypes, among which clear cell renal cell carcinoma (ccRCC) is one of the most common causes of tumor-related deaths. However, the molecular mechanism of ccRCC remains unclear. In order to identify the candidate genes that may exist in the occurrence and development of ccRCC, microarray datasets GSE6344, GSE16441, GSE36895, GSE53757 and GSE76351 had been downloaded from Gene ExpressionOmnibus (GEO) database. Apart from that, the differentially expressed genes (DEGs) were screened through Bioinformatics & Evolutionary Genomics. In addition, the protein-protein interaction network (PPI) was constructed, and the module analysis was performed using STRING and Cytoscape. By virtue of DAVID online database, GO/KEGG enrichment analysis of DEGs was performed. Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes. The plug-in MCODE of Cytoscape was adopted to analyze the most significant modules of DEGs. What's more, the genes with degree greater than 10 in DEGs were selected as the hub genes. The overall survival (OS) and disease progression free survival (DFS) of 9 hub genes were analyzed through GEPIA2 online platform. As shown by the survival analysis, SLC34A1, SLC12A3, SLC12A1, PLG, and ENO2 were closely related to theOS of ccRCC, whereas SLC34A1 and LOX were closely related to DFS. Among 11 SLC members, 6 SLC members were highly expressed in non-cancerous tissues (SLC5A2, SLC12A1, SLC12A3, SLC34A1, SLC34A2, SLC34A3). Besides, SLC12A5 and SLC12A7 were highly expressed in ccRCC. Furthermore, SLC12A1-A7, SLC34A1 and SLC34A3 were closely related to OS, whereas SLC12A2/A4/A6/A7 and SLC34A1/A3 were closely related to DFS. In addition, 5 algorithms were used to analyze hub genes, the overlapping geneswere AQP2 and KCNJ1. To sumup, hub gene can help us understand the molecular mechanism of the occurrence and development of ccRCC, thereby providing a theoretical basis for the diagnosis and targeted therapy of ccRCC. [ABSTRACT FROM AUTHOR]

Ganoderic acid Me (GA-Me) is a natural bioactive compound derived from Ganoderma lucidum. Our present results suggested that GA-Me inhibited proliferation, induced DNA fragmentation and significantly activated caspase-9 and caspase-3 in HCT116 cells. As shown in our previous studies, GA-Me targets several genes to prevent cancer, including colorectal cancer (CRC). Thus, we hypothesized that GA-Me might be a multitarget ligand against cancer. However, its exact mechanism in CRC remains unclear. Here, whole-transcriptome sequencing was employed to assess the long noncoding RNA (lncRNA), circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) profiles of GA-Me-treated HCT116 cells. In total, 1572 differentially expressed (DE) lncRNAs, 123 DEcircRNAs, 87 DEmiRNAs, and 1508 DEmRNAs were identified. DCBLD2 and RAPGEF5 were validated as two core mRNAs in the DElncRNA, DEcircRNA, and DEmiRNA networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed the biological functions and potential mechanisms of TCONS-00008997, XR-925056.2, circRNA-07908, hsa-miR-100-3p, hsa-miR-1257, hsa-miR-3182, NAV3, ADAM20, and STARD4, which were altered after GA-Me treatment. The regulatory relationships of the XR-925056.2-hsa-miR-3182-NAV3/ADAM20/STARD4, circRNA-07908|Chr22:38986298-39025349-hsa-miR-3182-NAV3/ADAM20, ENST00000414039/ENST00000419190-novel874_mature-MMP9 and circRNA-00314|Chr1:35470863-35479212/circRNA-05460|Chr17:72592203-72649268-novel874_mature-MMP9 immune-regulatory networks involved both noncoding RNAs (ncRNAs) and mRNAs. Molecular docking studies showed that Zn2+ and the His201, His205, His211, Glu202, and Ala165 residues of MMP2 contributed to its high affinity for GA-Me. Zn2+ and the Glu402 and Gly186 residues of MMP9 are important for its interaction with GA-Me. Our results suggested and confirmed that GA-Me is a potential multitarget lead compound for CRC treatment with unique polypharmacological advantages. [ABSTRACT FROM AUTHOR]

Ganoderic acid Me (GA-Me) is a natural bioactive compound derived from Ganoderma lucidum. Our present results suggested that GA-Me inhibited proliferation, induced DNA fragmentation and significantly activated caspase-9 and caspase-3 in HCT116 cells. As shown in our previous studies, GA-Me targets several genes to prevent cancer, including colorectal cancer (CRC). Thus, we hypothesized that GA-Me might be a multitarget ligand against cancer. However, its exact mechanism in CRC remains unclear. Here, whole-transcriptome sequencing was employed to assess the long noncoding RNA (lncRNA), circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) profiles of GA-Me-treated HCT116 cells. In total, 1572 differentially expressed (DE) lncRNAs, 123 DEcircRNAs, 87 DEmiRNAs, and 1508 DEmRNAs were identified. DCBLD2 and RAPGEF5 were validated as two core mRNAs in the DElncRNA, DEcircRNA, and DEmiRNA networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed the biological functions and potential mechanisms of TCONS-00008997, XR-925056.2, circRNA-07908, hsa-miR-100-3p, hsa-miR-1257, hsa-miR-3182, NAV3, ADAM20, and STARD4, which were altered after GA-Me treatment. The regulatory relationships of the XR-925056.2-hsa-miR-3182-NAV3/ADAM20/STARD4, circRNA-07908|Chr22:38986298-39025349-hsa-miR-3182-NAV3/ADAM20, ENST00000414039/ENST00000419190-novel874_mature-MMP9 and circRNA-00314|Chr1:35470863-35479212/circRNA-05460|Chr17:72592203-72649268-novel874_mature-MMP9 immune-regulatory networks involved both noncoding RNAs (ncRNAs) and mRNAs. Molecular docking studies showed that Zn2+ and the His201, His205, His211, Glu202, and Ala165 residues of MMP2 contributed to its high affinity for GA-Me. Zn2+ and the Glu402 and Gly186 residues of MMP9 are important for its interaction with GA-Me. Our results suggested and confirmed that GA-Me is a potential multitarget lead compound for CRC treatment with unique polypharmacological advantages.

Bladder cancer (BC) is the most common malignant tumor of the urinary system and is associated with high morbidity and mortality; however, the molecular mechanism underlying its occurrence is not clear. In this study, the gene expression profile and related clinical information of GSE13507 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing (RNA-seq) expression data and related clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Overlapping genes were identified by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Then, we carried out functional enrichment analysis to understand the potential biological functions of these co-expressed genes. Finally, we performed a protein–protein interaction (PPI) analysis combined with survival analysis. Using the CytoHubba plug-in of Cytoscape, TROAP , CENPF , PRC1 , AURKB , CCNB2 , CDC20 , TTK , CEP55 , ASPM , and CDCA8 were identified as candidate central genes. According to the survival analysis, the high expression of TTK was related to the poor overall survival (OS) of patients with BC. TTK may also affect the bladder tumor microenvironment (TME) by affecting the number of immune cells. The expression level of TTK was verified by immunohistochemistry (IHC) and real-time quantitative polymerase chain reaction (RT-qPCR), and the tumor-promoting effect of TTK in BC cells was confirmed in vitro. Our results also identified the MSC-AS1/hsa-miR-664b-3p/TTK regulatory axis, which may also play an important role in the progression of BC, but further research is needed to verify this result. In summary, our results provide a new idea for accurate early diagnosis, clinical treatment, and prognosis of BC [ABSTRACT FROM AUTHOR]

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC) is the most common malignant tumor of the urinary system and is associated with high morbidity and mortality; however, the molecular mechanism underlying its occurrence is not clear. In this study, the gene expression profile and related clinical information of GSE13507 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing (RNA-seq) expression data and related clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Overlapping genes were identified by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Then, we carried out functional enrichment analysis to understand the potential biological functions of these co-expressed genes. Finally, we performed a protein–protein interaction (PPI) analysis combined with survival analysis. Using the CytoHubba plug-in of Cytoscape, TROAP, CENPF, PRC1, AURKB, CCNB2, CDC20, TTK, CEP55, ASPM, and CDCA8 were identified as candidate central genes. According to the survival analysis, the high expression of TTK was related to the poor overall survival (OS) of patients with BC. TTK may also affect the bladder tumor microenvironment (TME) by affecting the number of immune cells. The expression level of TTK was verified by immunohistochemistry (IHC) and real-time quantitative polymerase chain reaction (RT-qPCR), and the tumor-promoting effect of TTK in BC cells was confirmed in vitro. Our results also identified the MSC-AS1/hsa-miR-664b-3p/TTK regulatory axis, which may also play an important role in the progression of BC, but further research is needed to verify this result. In summary, our results provide a new idea for accurate early diagnosis, clinical treatment, and prognosis of BC

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC.

PurposeThe molecular mechanism underlying the carcinogenesis and development of lung squamous cell carcinoma (LUSC) has not been sufficiently elucidated. This analysis was performed to find pivotal genes and explore their prognostic roles in LUSC.MethodsA microarray dataset from GEO (GSE19188) and a TCGA-LUSC dataset were used to identify differentially co-expressed genes through Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We conducted functional enrichment analyses of differentially co-expressed genes and established a protein-protein interaction (PPI) network. Then, we identified the top 10 hub genes using the Maximal Clique Centrality (MCC) algorithm. We performed overall survival (OS) analysis of these hub genes among LUSC cases. GSEA analyses of survival-related hub genes were conducted. Ultimately, the GEO and The Human Protein Atlas (THPA) databases and immunohistochemistry (IHC) results from the real world were used to verify our findings.ResultsA list of 576 differentially co-expressed genes were selected. Functional enrichment analysis indicated that regulation of vasculature development, cell−cell junctions, actin binding and PPAR signaling pathways were mainly enriched. The top 10 hub genes were selected according to the ranking of MCC scores, and 5 genes were closely correlated with OS of LUSC. Additionally, GSEA analysis showed that spliceosome and cell adhesion molecules were associated with the expression of GNG11 and ADCY4, respectively. The GSE30219 and THPA databases and IHC results from the real world indicated that although GNG11 was not detected, ADCY4 was obviously downregulated in LUSC tissues at the mRNA and protein levels.ConclusionsThis analysis showed that survival-related hub genes are highly correlated to the tumorigenesis and development of LUSC. Additionally, ADCY4 is a candidate therapeutic and prognostic biomarker of LUSC.

Purpose: The molecular mechanism underlying the carcinogenesis and development of lung squamous cell carcinoma (LUSC) has not been sufficiently elucidated. This analysis was performed to find pivotal genes and explore their prognostic roles in LUSC. Methods: A microarray dataset from GEO (GSE19188) and a TCGA-LUSC dataset were used to identify differentially co-expressed genes through Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We conducted functional enrichment analyses of differentially co-expressed genes and established a protein-protein interaction (PPI) network. Then, we identified the top 10 hub genes using the Maximal Clique Centrality (MCC) algorithm. We performed overall survival (OS) analysis of these hub genes among LUSC cases. GSEA analyses of survival-related hub genes were conducted. Ultimately, the GEO and The Human Protein Atlas (THPA) databases and immunohistochemistry (IHC) results from the real world were used to verify our findings. Results: A list of 576 differentially co-expressed genes were selected. Functional enrichment analysis indicated that regulation of vasculature development, cell−cell junctions, actin binding and PPAR signaling pathways were mainly enriched. The top 10 hub genes were selected according to the ranking of MCC scores, and 5 genes were closely correlated with OS of LUSC. Additionally, GSEA analysis showed that spliceosome and cell adhesion molecules were associated with the expression of GNG11 and ADCY4, respectively. The GSE30219 and THPA databases and IHC results from the real world indicated that although GNG11 was not detected, ADCY4 was obviously downregulated in LUSC tissues at the mRNA and protein levels. Conclusions: This analysis showed that survival-related hub genes are highly correlated to the tumorigenesis and development of LUSC. Additionally, ADCY4 is a candidate therapeutic and prognostic biomarker of LUSC. [ABSTRACT FROM AUTHOR]

Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone cancer and it occurs predominantly in children and adolescents. OSA is associated with a poor prognosis and highest cause of cancer-related death. However, there are a few biomarkers that can serve as reasonable assessments of prognosis.Methods: Gene expression profiling data were downloaded from dataset GSE39058 and GSE21257 from the Gene Expression Omnibus database as well as TARGET database. Bioinformatic analysis with data integration was conducted to discover the significant biomarkers for predicting prognosis. Verification was conducted by qPCR and western blot to measure the expression of genes.Results: 733 seed genes were selected by combining the results of the expression profiling data with hub nodes in a human protein-protein interaction network with their gene functional enrichment categories identified. Following by Cox proportional risk regression modeling, a 2-gene (PML-EPB41) signature was developed for prognostic prediction of patients with OSA. Patients in the high-risk group had significantly poorer survival outcomes than in the low-risk group. Finally, the signature was validated and analyzed by the external dataset along with Kaplan–Meier survival analysis as well as biological experiment. A molecular gene model was built to serve as an innovative predictor of prognosis for patients with OSA.Conclusion: Our findings define novel biomarkers for OSA prognosis, which will possibly aid in the discovery of novel therapeutic targets with clinical applications.

Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone cancer and it occurs predominantly in children and adolescents. OSA is associated with a poor prognosis and highest cause of cancer-related death. However, there are a few biomarkers that can serve as reasonable assessments of prognosis. Methods: Gene expression profiling data were downloaded from dataset GSE39058 and GSE21257 from the Gene Expression Omnibus database as well as TARGET database. Bioinformatic analysis with data integration was conducted to discover the significant biomarkers for predicting prognosis. Verification was conducted by qPCR and western blot to measure the expression of genes. Results: 733 seed genes were selected by combining the results of the expression profiling data with hub nodes in a human protein-protein interaction network with their gene functional enrichment categories identified. Following by Cox proportional risk regression modeling, a 2-gene (PML-EPB41) signature was developed for prognostic prediction of patients with OSA. Patients in the high-risk group had significantly poorer survival outcomes than in the low-risk group. Finally, the signature was validated and analyzed by the external dataset along with Kaplan–Meier survival analysis as well as biological experiment. A molecular gene model was built to serve as an innovative predictor of prognosis for patients with OSA. Conclusion: Our findings define novel biomarkers for OSA prognosis, which will possibly aid in the discovery of novel therapeutic targets with clinical applications. [ABSTRACT FROM AUTHOR]

Keywords: osteosarcoma; protein-protein interaction network; gene signature; prognostic prediction; survival analysis EN osteosarcoma protein-protein interaction network gene signature prognostic prediction survival analysis N.PAG N.PAG 1 04/26/21 20210414 NES 210414 In the original article, there was an error. In this dataset, each patient sample included detailed clinicopathologic information and survival status. Osteosarcoma, protein-protein interaction network, gene signature, prognostic prediction, survival analysis. [Extracted from the article]