The identification of new biomarkers for bladder cancer: A study based on TCGA and GEO datasets

Hu, Junyi; Zhou, Lijie; Song, Zhengshuai; Xiong, Momiao; Zhang, Youpeng; Yu, Yang; Chen, Ke; Chen, Zhaohui

doi:10.1002/jcp.28208

Cited by 35 publications

(40 citation statements)

References 42 publications

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“…MYH11 is a protein that participates in muscle contraction through the hydrolysis of adenosine triphosphate [28]. Its expression have been found to be associated with many kinds of cancer, such as lung cancer, bladder cancer [29], head and neck cancer [30] and colorectal cancer [31]. RARRES1 is also gene that dysregulated in several cancers.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis reveals the ceRNA co-expression network in tumor microenvironment and prognostic biomarkers in Sarcomas

Zou¹,

Wang²,

Zhao³

et al. 2020

Preprint

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Background Sarcomas (SARCs) are rare, heterogeneous mesenchymal neoplasia. To understand the tumor microenviroment (TME) and identify potential biomarkers for prognosis that associated with TME of SARCs might provide effective clues for immune therapy. Methods We evaluated the immune scores and stromal scores by using the RNA sequencing dataset of SARCs patients from The Cancer Genome Atlas (TCGA) database and the ESTIMATE algorithm. Then the differentially expressed mRNAs (DEGs), miRNAs (DEMs) and lncRNAs (DELs) were filtered after comparing the two high- and low- scores groups. Next, based on these DERNAs, we established a competing endogenous RNA (ceRNA) network and explored the prognostic roles of biomarkers involved in the network with the help of bioinformatics analysis. Results High immune scores were significantly associated with favorable overall survival of SARCs patients. Next, a total of 328 DEGs, 18 DEMs and 67 DELs that commonly regulated in the immune and stromal scores groups were obtained. And for the DEGs, some of the Gene Ontology (GO) terms and pathways were mainly associated with immune processes. A ceRNA network were constructed with 142 nodes and 424 edges, in which hsa-miR-9-5p, hsa-miR-490-3p, hsa-miR-133a-3p, hsa-miR-133b and hsa-miR-129-5p were the top 5 nodes. Additionally, the protein-protein Interaction (PPI) network identified MMP9, TYROBP, CSF1, CXCR4, FBN1, FLNA, PDGFRB, CYBB, FCGR3A and MYH11 as hub nodes with considerable importance that functioned in the network. Finally, the Kaplan-Meier survival analysis demonstrated that 9 mRNAs (APOL1, EFEMP1, LYZ, MEDAG, MYH11, RARRES1, TNFAIP2, TNFSF10 and ZNF385A), 2 miRNAs (hsa-miR-9-5p and hsa-miR-183-5p) and 3 lncRNAs (CTD-2228K2.7, HOTAIRM1 and NCF1C) were closely associated with the overall survival of SARCs patients. Conclusions Taken together, our study confirmed that the prognosis value of immune scores for SARCs patients, also we identified a list of TME-related biomarkers which might contribute to prognostic prediction and help improve the efficacy of immune therapy.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis reveals the ceRNA co-expression network in tumor microenvironment and prognostic biomarkers in Sarcomas

Zou¹,

Wang²,

Zhao³

et al. 2020

Preprint

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“…We retrieved seven bioinformatics studies for BCa biomarkers based on public database analyses [9,[29][30][31][32][33][34], and we compared the biomarkers in the present study with the ones that were reported in the literature studies. We found that CRYAB and CASQ2 were so far unrecognized as biomarkers in previous studies.…”

Section: Literature Researchmentioning

confidence: 99%

“…The data collection process was undertaken and ended in November 2019. The retrieval criteria were rigorous, filtering with bioinformatics analysis and BCa, only 7 full-text papers were returned [9,[29][30][31][32][33][34]. On the basis of Oncomine database, 5 studies were relevant [35] and we gathered the information of the 14 hub genes from the 5 previous studies.…”

Section: Literature Retrieval and Oncomine Meta-analysismentioning

confidence: 99%

Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer

Zhang

Berndt‐Paetz

Neuhaus

2020

Data

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Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up-and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa. Dataset:The following are available online at

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“…Previous findings have shown that in aortic tissue, destruction of MYH11 can lead to vascular smooth muscle cell loss, disorganization and hyperplasia, which is one of the mechanisms leading to thoracic aortic aneurysms and dissections (6,7). In previous years, studies have indicated that mutations of the MYH11 gene are also associated with various types of cancer, including colorectal (8,9), bladder (10), laryngeal (11) and head and neck cancer (12). This may be due to the role of MYH11 role in cell migration, interaction with cell adhesion proteins and tumor suppression (13,14).…”

Section: Introductionmentioning

confidence: 99%

Clinical and prognostic significance of MYH11 in lung cancer

et al. 2020

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Myosin heavy chain 11 (MYH11), encoded by the MYH11 gene, is a protein that participates in muscle contraction through the hydrolysis of adenosine triphosphate. Although previous studies have demonstrated that MYH11 gene expression levels are downregulated in several types of cancer, its expression levels have rarely been investigated in lung cancer. The present study aimed to explore the clinical significance and prognostic value of MYH11 expression levels in lung cancer and to further study the underlying molecular mechanisms of the function of this gene. The Oncomine database showed that the MYH11 expression levels were decreased in lung cancer compared with those noted in the normal lung tissue (P<0.05). Kaplan-Meier plotter results revealed that the decreased MYH11 expression levels were correlated with poor prognosis in lung cancer patients. Among the lung cancer cases with gene alteration of MYH11, mutation was the most common of all alteration types. Coexpedia and Metascape analyses revealed that the target genes were primarily enriched in 'muscle contraction', 'contractile fiber part', 'actin cytoskeleton' and the 'adherens junction'. These results indicated that MYH11 is a potential novel drug target and prognostic indicator of lung cancer.

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The identification of new biomarkers for bladder cancer: A study based on TCGA and GEO datasets

Cited by 35 publications

References 42 publications

Bioinformatics analysis reveals the ceRNA co-expression network in tumor microenvironment and prognostic biomarkers in Sarcomas

Bioinformatics analysis reveals the ceRNA co-expression network in tumor microenvironment and prognostic biomarkers in Sarcomas

Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer

Clinical and prognostic significance of MYH11 in lung cancer

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