Dataset Information

Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis.

ABSTRACT: Bladder transitional cell carcinoma (BTCC) forms more than 90% of bladder cancer cases. It brings challenges to the early diagnosis and therapy of BTCC, due to lack of efficient screening biomarkers. We used weighted gene co-expression network analysis (WGCNA) combined competing endogenous RNA (ceRNA) network construction depending on TCGA datasets to investigate potential hub genes and regulatory pathways associated with occurrence and progression of BTCC. We further used real-time polymerase chain reaction (RT-PCR) to validate the relative expression genes correlated with BTCC. By WGCNA, the gene co-expression module with 11 genes was found corelated with BTCC tumour stage and prognosis after survival analyses. Ultimately, we put 100 highly stage-related genes into the above constructed ceRNA network and then constructed another new network. Among them, all elements in AC112721.1/LINC00473/AC128709.1-hsa-mir-195-RECK and LINC00460-hsa-mir-429-ZFPM2 axes were simultaneously corelated with overall survival. RT-PCR showed that AKAP12 was downregulated in tumour tissues. The hub genes screened out in the present study may provide ideals for further treatment on BTCC.

SUBMITTER: Wang H

PROVIDER: S-EPMC10902574 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis.

Wang Hai H Gao Lei L Chen Yin Y Zhang Lei L Bai Yu Y Zhao Cuiping C Zhang Lifeng L Zuo Li L Sun Heyun H

Journal of cellular and molecular medicine 20231005 5

Bladder transitional cell carcinoma (BTCC) forms more than 90% of bladder cancer cases. It brings challenges to the early diagnosis and therapy of BTCC, due to lack of efficient screening biomarkers. We used weighted gene co-expression network analysis (WGCNA) combined competing endogenous RNA (ceRNA) network construction depending on TCGA datasets to investigate potential hub genes and regulatory pathways associated with occurrence and progression of BTCC. We further used real-time polymerase c ...[more]

PMID: 37795791

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Project description:Despite aggressive treatment approaches, muscle-invasive bladder urothelial carcinoma (MIBC) patients still have a 50% chance of developing general incurable metastases. Therefore, there is an urgent need for candidate markers to enhance diagnosis and generate effective treatments for this disease. We evaluated four mRNA microarray datasets to find differences between non-MIBC (NMIBC) and MIBC tissues. Through a gene expression profile analysis via the Gene Expression Omnibus database, we identified 56 differentially expressed genes (DEGs). Enrichment analysis of gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome pathways revealed the interactions between these DEGs. Next, we established a protein-protein interaction network to determine the interrelationship between the DEGs and selected 10 hub genes accordingly. Bladder urothelial carcinoma (BLCA) patients with COL1A2, COL5A1, and COL5A2 alterations showed poor disease-free survival rates, while BLCA patients with COL1A1 and LUM alterations showed poor overall survival rates. Oncomine analysis of MIBC versus NMIBC tissues showed that COL1A1, COL5A2, COL1A2, and COL3A1 were consistently among the top 20 overexpressed genes in different studies. Using the TCGAportal, we noted that the high expression of each of the four genes led to shorter BLCA patient overall survival. It was evident that BLCA patients with an elevated high combined gene expression had significantly shorter overall survival and relapse-free survival than those with low combined gene expression using PROGgeneV2. Using Gene Expression Profiling Interactive Analysis, we noted that COL1A1, COL1A2, COL3A1, and COL5A2 were positively correlated with each other in BLCA. These genes are considered as clinically relevant genes, suggesting that they may play an important role in the carcinogenesis, development, invasion, and metastasis of MIBC. However, considering we adopted a bioinformatic approach, more research is crucial to confirm our results. Nonetheless, our findings may have important prospective clinical implementations.

Project description:The incidence of autism spectrum disorder (ASD) is increasing year by year in children. The aim of the study was to find possible biomarkers for ASD diagnosis as well as examine MicroRNA (miRNA) signatures and crucial pathways. We conducted a two-stage study to explore potential target genes and functional miRNAs. Peripheral blood samples of children with ASD were enrolled and performed RNA sequencing analysis. The overlapped candidate genes were further screened in combination with differentially expressed genes (DEGs) of GSE77103 datasets. STRING established a protein-protein interaction network comprising DEGs. The hub genes were filtered out using the CytoHubba. Then, we set up a miRNA-mRNA regulatory network. Correlational analyses between hub genes and immune cells associated with ASD were carried out using the CIBERSORT software to assess the diversity of immune cell types in ASD. RNA-sequencing analysis was used to confirm the differential expression of 3 hub genes. Briefly, after blood samples were sequenced interrogating 867 differential genes in our internal screening dataset. After screening GEO databases, 551 DEGs obtained from GSE77103. Fourteen common genes were overlapped through DEGs of GEO datasets and internal screening dataset. Among protein-protein interaction network, 10 hub genes with high degree algorithm were screened out and 3 hub genes of them - ADIPOR1, LGALS3, and GZMB - that were thought to be most associated with the emergence of ASD. Then, we developed a network of miRNA-mRNA regulatory interactions by screening miRNAs (such as hsa-miR-20b-5p, hsa-miR-17-5p, and hsa-miR-216b-5p) that were closely associated to 3 hub genes. Additionally, we discovered 18 different immune cell types associated with ASD using the CIBERSORT algorithm, and we discovered that mononuclear macrophages differed considerably between the 2 groups. Overall, 3 hub genes (ADIPOR1, LGALS3, and GZMB) and 15 candidates miRNAs-target 3 genes regulatory pathways representing potentially novel biomarkers of ASD diseases were revealed. These findings could enhance our knowledge of ASD and offer possible therapeutic targets of ASD patients in the future.

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Project description:BackgroundsApproximately 75% of bladder cancer occurrences are of the non-muscle-invasive type. The estimated five-year survival rate is 26%-55%. Currently, there is no reliable biomarker available for early diagnosis and prognosis of bladder cancer. The present study aims to identify a biomarker using bioinformatic approaches to provide a new insight in clinical research for early diagnosis and prognosis of bladder cancer.MethodsClinical data and a transcriptome of bladder cancer were obtained from TCGA, GEO, GETx, and UCSC Xena. The differential expressed gene (DEG) analysis, weighted gene co-expression network analysis (WGCNA), and survival analysis using the Kaplan-Meier and Cox proportional-hazards models were used to identify the Microtubule-associated Proteins 1A (MAP1A). on overall survival (OS) and disease-free survival (DFS) was analyzed using GEPIA and GETx databases. The TIMER 2.0 database predicted the correlation between MAP1A and immunocytes and immune checkpoints. Target prediction of the regulated competing endogenous RNAs (ceRNAs) network of MAP1A was performed using starBase and TargetScan. Cystoscope v3.7.2 software was used to visualize the ceRNA coexpression network. The R programming language v4.0.2 was applied as an analytic tool. Gene expression of MAP1A verified by RT-qPCR.ResultsThe low expression of MAP1A was verified in bladder cancer tissues and bladder cancer cell lines SW780 and 5637. P < 0.001 were obtained by Kaplan-Meier survival analysis and Cox proportional hazards model, with a hazard ratio (HR) of 1.4. Significant correlations between MAP1A and OS (P < 0.001, HR = 1.9) as well as DFS (P < 0.05, HR = 1.7) in bladder cancer were identified through gene expression profiling interactive analysis (GEPIA), indicating MAP1A may be a high-risk factor. Significant correlation in single copy-number variation of MAP1A gene with CD8+ T cells, and myeloid dendritic cells (MDCs) (P < 0.05) was noted. MAP1A expression was shown to be significantly correlated with the amount of CD4+ T cells and CD8+ T cells, MDCs, macrophages, and neutrophils in a statistically significant positive manner (P < 0.001). However, the MAP1A expression demonstrated a strong negative connection with B cells (P < 0.001). Except for macrophage M1 genes IRF5 and PTGS2, MAP1A expression was significantly correlated with the gene levels in immunocytes such as CD4+ T cells, CD8+ T cells, B cells, dendritic cells (DCs), macrophages, and neutrophils (Cor > 0.2, P < 0.001), as well as immune checkpoint related genes including cytotoxic t-lymphocyte-associated protein 4 (CTLA-4), programmed death 1 (PD-1), programmed death ligand 1 (PD-L1) (P < 0.001). Finally, we predicted that the MAP1A-interacting miRNA was miR-34a-5p, and the MAP1A endogenous competing RNAs were LNC00667, circ_MAP1B, and circ_MYLK, respectively. These findings support the need for further studies on the mechanism underlying the pathogenesis of this disease.ConclusionMAP1A is considered as a prospective biomarker for early diagnosis, therapeutic observation, and prognosis analysis in bladder cancer.

Dataset Information

Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis.

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Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis.

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