Mass spectrometric detection combined with bioinformatic analysis identified possible protein markers and key pathways associated with bladder cancer

Wu, Zhi‐Ying; Wang, Sugui; Jiang, Fujin; Li, Qiang; Wang, Chao; Wang, Huanqiang; Zhang, Wei; Xue, Peng; Wang, Shoulin

doi:10.1016/j.gene.2017.05.054

Cited by 8 publications

(8 citation statements)

References 25 publications

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“… Lee et al (2013) found that lung adenocarcinoma patients with high expression levels of ACTA2 have significantly increased distant metastasis and poor prognosis. Wu et al (2017) conducted mass spectrometry and bioinformatics analyses and found that ACTA2 might play an important role in the progression of bladder cancer, which is consistent with our study, but the specific regulatory mechanisms and prognosis associated with ACTA2 in bladder cancer remain not fully understood. The ubiquitin-conjugating enzyme E2C (UBE2C) plays a key role in the regulation of mitotic processes.…”

Section: Discussionsupporting

confidence: 89%

Identification of key candidate genes and biological pathways in bladder cancer

Gào¹,

Chen²,

Chen³

et al. 2018

PeerJ

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BackgroundBladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms.MethodsThe , , and expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer.ResultsIn total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA.ConclusionsThis study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

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Section: Discussionsupporting

confidence: 89%

Identification of key candidate genes and biological pathways in bladder cancer

Gào¹,

Chen²,

Chen³

et al. 2018

PeerJ

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“…A total of 7 KEGG pathways were enriched and with P<0.05 in the current study. The ECM-receptor interaction pathway has been demonstrated to be involved in the tumorigenesis of various cancers (54–56) and in the current study, the KEGG pathway analysis indicated that these DEGs were also enriched in the ECM-receptor interaction pathway. The PI3K-Akt signaling pathway contributes to the regulatory function in cell survival, proliferation, metastasis, metabolism, angiogenesis and apoptosis (57–60).…”

Section: Discussionsupporting

confidence: 56%

Bioinformatics analysis identifies hub genes and pathways in nasopharyngeal carcinoma

et al. 2019

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The aim of the present study was to identify genes associated with and the underlying mechanisms in nasopharyngeal carcinoma (NPC) using microarray data. GSE12452 and GSE34573 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was utilized to obtain differentially expressed genes (DEGs). In addition, the Database for Annotation, Visualization and Integrated Discovery was used to perform pathway enrichment analyses for DEGs using the Gene Ontology (GO) annotation along with the Kyoto Encyclopedia of Genes and Genomes (KEGG). Furthermore, Cytoscape was used to perform module analysis of the protein-protein interaction (PPI) network and pathways of the hub genes were studied. A total of 298 genes were ascertained as DEGs in the two datasets. To functionally categorize these DEGs, we obtained 82 supplemented GO terms along with 7 KEGG pathways. Subsequently, a PPI network consisting of 10 hub genes with high degrees of interaction was constructed. These hub genes included cyclin-dependent kinase (CDK) 1, structural maintenance of chromosomes (SMC) 4, kinetochore-associated (KNTC) 1, kinesin family member (KIF) 23, aurora kinase A (AURKA), ATAD (ATPase family AAA domain containing) 2, NDC80 kinetochore complex component, enhancer of zeste 2 polycomb repressive complex 2 subunit, BUB1 mitotic checkpoint serine/threonine kinase and protein regulator of cytokinesis 1. CDK1, SMC4, KNTC1, KIF23, AURKA and ATAD2 presented with high areas under the curve in receiver operator curves, suggesting that these genes may be diagnostic markers for nasopharyngeal carcinoma. In conclusion, it was proposed that CDK1, SMC4, KNTC1, KIF23, AURKA and ATAD2 may be involved in the tumorigenesis of NPC. Furthermore, they may be utilized as molecular biomarkers in early diagnosis of NPC.

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“…There may be multiple molecular subtypes in the same tumor, which may be an important factor affecting the accuracy of the molecular subtyping prediction system. With the rapid development of single-cell high-throughput sequencing (46,55,56), mass spectrometric detection (57), immune analysis (58,59), and other technologies, the accuracy of the molecular subtyping prediction system will be further improved (Figure 4). Compared with traditional pathological classification, molecular subtyping offers a more comprehensive analysis of the genome, transcriptome and non-coding RNA, providing important guidance for BC adjuvant chemotherapy, targeted therapy and immunotherapy.…”

Section: Mibc Molecular Subtypingmentioning

confidence: 99%

Traditional Classification and Novel Subtyping Systems for Bladder Cancer

Zhu

Xiao-mi

et al. 2020

Front. Oncol.

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Bladder cancer is the most common tumor in the urinary system, with approximately 420,000 new cases and 160,000 deaths per year. The European Organization for Research and Treatment of Cancer (EOTRC) classifies non-muscular invasive bladder cancer (NMIBC) into low-risk, medium-risk and high-risk groups based on a comprehensive analysis of NMIBC pathological parameters and the risk of recurrence and progression to muscular invasive bladder cancer (MIBC). Traditional classification systems are based on pathologic grading, staging systems, and clinical prognosis. However, the pathological parameters of the tumor cannot fully reflect the "intrinsic characteristics" of bladder cancer, and tumors with a similar pathology exhibit different biological behaviors. Furthermore, although the traditional classification system cannot accurately predict the risk of recurrence or the progression of bladder cancer patients (BCs) individually, this method is widely used in clinical practice because of its convenient operation. With the development of sequencing and other technologies, the genetics-based molecular subtyping of bladder cancer has become increasingly studied. Compared with the traditional classification system, it provides more abundant tumor biological information and is expected to assist or even replace the traditional typing system in the future.

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Mass spectrometric detection combined with bioinformatic analysis identified possible protein markers and key pathways associated with bladder cancer

Cited by 8 publications

References 25 publications

Identification of key candidate genes and biological pathways in bladder cancer

Identification of key candidate genes and biological pathways in bladder cancer

Bioinformatics analysis identifies hub genes and pathways in nasopharyngeal carcinoma

Traditional Classification and Novel Subtyping Systems for Bladder Cancer

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