Dataset Information

A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival.

ABSTRACT:

Background

Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA.

Methods

Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognostic biomarkers screened by Cox proportional hazard regression model and random forest were used to construct a risk score model that can predict the prognosis of patients with BLCA. The GSE13507 dataset was used as the independent testing dataset to test the performance of the risk score model in predicting the prognosis of patients with BLCA.

Results

WGCNA identified seven co-expression modules, in which the brown module consisted of 77 genes was most significantly correlated with the pathological stage of BLCA. Cox proportional hazard regression model and random forest identified TPST1 and P3H4 as prognostic biomarkers. Elevated TPST1 and P3H4 expressions were associated with the high pathological stage and worse survival. The risk score model based on the expression level of TPST1 and P3H4 outperformed pathological stage indicators and previously proposed prognostic models.

Conclusion

The gene co-expression network-based study could provide additional insight into the tumorigenesis and progression of BLCA, and our proposed risk score model may aid physicians in the assessment of the prognosis of patients with BLCA.

SUBMITTER: Chen Z

PROVIDER: S-EPMC6617625 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival.

Chen Zihao Z Liu Guojun G Hossain Aslam A Danilova Irina G IG Bolkov Mikhail A MA Liu Guoqing G Tuzankina Irina A IA Tan Wanlong W

Hereditas 20190709

<h4>Background</h4>Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA.<h4>Methods</h4>Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognos ...[more]

PMID: 31333338

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Project description:INTRODUCTION: We previously identified common differentially expressed (DE) genes in bladder cancer (BC). In the present study we analyzed in depth, the expression of several groups of these DE genes. MATERIALS AND METHODS: Samples from 30 human BCs and their adjacent normal tissues were analyzed by whole genome cDNA microarrays, qRT-PCR and Western blotting. Our attention was focused on cell-cycle control and DNA damage repair genes, genes related to apoptosis, signal transduction, angiogenesis, as well as cellular proliferation, invasion and metastasis. Four publicly available GEO Datasets were further analyzed, and the expression data of the genes of interest (GOIs) were compared to those of the present study. The relationship among the GOI was also investigated. GO and KEGG molecular pathway analysis was performed to identify possible enrichment of genes with specific biological themes. RESULTS: Unsupervised cluster analysis of DNA microarray data revealed a clear distinction in BC vs. control samples and low vs. high grade tumors. Genes with at least 2-fold differential expression in BC vs. controls, as well as in non-muscle invasive vs. muscle invasive tumors and in low vs. high grade tumors, were identified and ranked. Specific attention was paid to the changes in osteopontin (OPN, SPP1) expression, due to its multiple biological functions. Similarly, genes exhibiting equal or low expression in BC vs. the controls were scored. Significant pair-wise correlations in gene expression were scored. GO analysis revealed the multi-facet character of the GOIs, since they participate in a variety of mechanisms, including cell proliferation, cell death, metabolism, cell shape, and cytoskeletal re-organization. KEGG analysis revealed that the most significant pathway was that of Bladder Cancer (p?=?1.5×10(-31)). CONCLUSIONS: The present work adds to the current knowledge on molecular signature identification of BC. Such works should progress in order to gain more insight into disease molecular mechanisms.

Project description:BackgroundCurrent diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays.PurposeOur goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays.Methodology/principal findingsBC samples and controls, both experimental and publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually, as well as in each tumor group. A dual analysis strategy was followed. First, experimental samples were analyzed and conclusions were formulated; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search of common DE genes. The experimental dataset identified 831 genes that were DE in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of samples from all microarray platforms revealed 17 common DE genes, (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) 4 of which participate in numerous pathways.Conclusions/significanceThe identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers.

Project description:Primary glioblastoma(pGBM) is the most malignant tumor of the central nervous system. Radiotherapy, chemotherapy and surgical treatment have little effect on the survival of pGBM patients. The prognosis is often poorly once the tumor recurs. It is urgent to develop new therapies for patients. In recent years, studies have been clarified that miRNA have a powerful regulating effect on the genes. However, the main group of miRNAs in regulating long-term survival specific related genes of pGBM is still unclear. Given that the survival period of most glioma patients is relatively short, studying long-term survival patients with pGBM is of great value for this disease. Our study aim to identify key miRNAs with long-term survival related genes present in pGBM and uncover their potential mechanisms. The gene expression profiles of GSE53733, GSE15824, GSE30563, GSE50161 were obtained from the Gene Expression Omnibus database. Firstly, samples were divided into 3 groups according to its survival time and each group compare to the normal control group. Then we obtained differential expression genes (DEGs) with a long-term survival specific (LTSDEGs) and a short-term survival specific DEGs (STSDEGs). Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted with LTSDEGs and STSDEGs together. Moreover, we used the UALCAN database to verify LTSDEGs and STSDEGs, and obtained long-term verified survival specific DEGs(LTVSDEGs) and short-term verified survival specific DEGs(STVSDEGs). Finally, we established the predicted key miRNAs-LTVSDEGs interaction network. The protein expressions of the top 4 LTVSDEGs were verified in the HPA database with immunohistochemical staining. In total, we found 260 genes changed in LTSDEGs and 822 genes changed in STSDEGs. GO and KEGG results shown that the major changes are focused on tumor metabolism. 9 LTVSDEGs and 18 STVSDEGs were verified in UALCAN database. As for protein expression verification in top 4 LTVSDEGs, ZNF630, BLVRB and RPA3 were verified, while TPBG was not detected. We obtained 59 key miRNA from the predicted key miRNAs-LTVSDEGs interaction network. 25 key miRNAs were verified using GSE90603. Finally, we constructed the key miRNAs-LTVSDEGs network using a Sankey diagram, including 25 miRNAs and 7 LTVSDEGs. In conclusion, our study shows that there is a close relationship between metabolic changes and survival in pGBM. Besides, we established a key miRNAs-LTVSDEGs network for pGBM, which could be the key path in prolonging the life of pGBM patients.

Project description:Bladder cancer (BCa) is the tenth most common tumor in humans. DNA damage repair genes (DDRGs) play important roles in many malignant tumors; thus, their functions in BCa should also be explored. We performed a comprehensive analysis of the expression profiles of DDRGs in 410 BCa tumors and 19 normal tissues from The Cancer Genome Atlas database. We identified 123 DDRGs differentially expressed between BCa tumors and normal tissues, including 95 upregulated and 28 downregulated genes. We detected 22 DDRGs associated with overall survival (OS) of patients with BCa by performing univariate Cox regression analysis. To explore the interactions between OS-associated DDRGs, we constructed a PPI network, which showed that the top six DDRGs (CDCA2, FOXM1, PBK, RRM2, ORC1, and HDAC4) with the highest scores in the PPI network might play significant roles in OS of BCa. Moreover, to investigate the latent regulatory mechanism of these OS-associated DDRGs, we analyzed the transcription factors (TFs)-DDRGs regulatory network. The core seven TFs (NCAPG, DNMT1, LMNB1, BRCA1, E2H2, CENPA, and E2F7) were shown to be critical regulators of the OS-related DDRGs. The 22 DDRGs were incorporated into a stepwise multivariable Cox analysis. Then, we built the index of risk score based on the expression of 8 DDRGs (CAD, HDAC10, JDP2, LDLR, PDGFRA, POLA2, SREBF1, and STAT1). The p-value < 0.0001 in the Kaplan-Meier survival plot and an area under the ROC curve (AUC) of 0.771 in TCGA-BLCA training dataset suggested the high specificity and sensitivity of the prognostic index. Furthermore, we validated the risk score in the internal TCGA-BLCA and an independent GSE32894 dataset, with AUC of 0.743 and 0.827, respectively. More importantly, the multivariate Cox regression and stratification analysis demonstrated that the predictor was independent of various clinical parameters, including age, tumor stage, grade, and number of positive tumor lymph nodes. In summary, a panel of 8 DNA damage repair genes associated with overall survival in bladder cancer may be a useful prognostic tool.

Dataset Information

A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival.

Background

Methods

Results

Conclusion

Publications

A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival.

Similar Datasets

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