Identification of key candidate genes and biological pathways in bladder cancer

Yuan

J Cellular Molecular Medi

et al. 2019

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan-cancer validation and time-dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed.Eight genes were eventually screened out as progression-related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E-MTAB-4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time-dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan-cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision-making. K E Y W O R D S bladder cancer, Gene Expression Omnibus (GEO), LASSO, prognosis, WGCNA S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Xiong Y, Yuan L, Xiong J, et al. An outcome model for human bladder cancer: A comprehensive study based on weighted gene co-expression network analysis.

Section: Discussionmentioning

confidence: 54%

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

Yuan

J Cellular Molecular Medi

et al. 2019

BioMed Research International

“…Microarray analysis is a new method to study tumor genes, search for molecular targets of tumor drug therapy and monitor prognosis. However, due to the heterogeneity of the experimental samples, different detection platforms and data processing methods generate inconsistent results [3]. The study was based on biological information in the GEO database, and the final results were obtained by analyzing the molecular functions of mRNA associated with thyroid cancer and the signal pathways involved.…”

Section: Introductionmentioning

confidence: 99%

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Shen

Dong

Liu

et al. 2020

Background. The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. Methods. The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. Results. By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. Conclusion. The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

J of Cellular Biochemistry

“…In recent years, the morbidity of bladder urothelial carcinoma has increased, and the recurrence rate is high. Nonmuscle‐invasive bladder urothelial carcinoma (NMIBC) accounts for approximately 75% of bladder urothelial carcinoma, and approximately 80% of NMIBC patients relapse within 5 years after initial treatment . The invasion and metastasis of bladder urothelial carcinoma are important causes of recurrence and death from bladder urothelial carcinoma .…”

Section: Introductionmentioning

confidence: 99%

Development of a novel six‐long noncoding RNA signature predicting survival of patients with bladder urothelial carcinoma

Gào

Zhang

Chen

et al. 2019

Self Cite

Bladder urothelial carcinoma is a malignant tumor with a high incidence in the uropoietic system. Considerable studies have shown that long noncoding RNA (lncRNA) plays an important role in the development and progression of bladder urothelial carcinoma. In this study, the lncRNA expression and clinical data of 377 bladder urothelial carcinoma patients were obtained from The Cancer Genome Atlas database and differentially expressed lncRNAs in cancer and normal groups were evaluated. Univariate COX and multivariate COX regression analyses of prognosis were performed on differentially expressed lncRNAs in the training data sets, six prognosis-related lncRNAs (LINC02195, LINC01484, LINC01468, SMC2-AS1, AC011298.1, and PTPRD-AS1) were assessed, and a six-lncRNA signature was constructed. The predictive capability of this six-lncRNA signature was validated in the testing data sets and entire data sets. The prognostic ability of the six-lncRNA signature was independent of other clinical elements after multivariate COX regression and stratified analyses of with other clinical elements. We performed functional enrichment analysis with the six prognosis-related lncRNAs. Results of functional enrichment revealed that these prognosis-related lncRNAs might promote the development and metastasis of bladder urothelial carcinoma. In summary, the six-lncRNA signature that we developed could effectively predict the prognosis of bladder urothelial carcinoma patients. This six-lncRNA signature might be a novel independent prognostic marker of bladder urothelial carcinoma. Moreover, it also provides novel insights into the mechanism of bladder urothelial carcinoma.