scholarly journals Extracellular matrix-related genes play an important role in the progression of NMIBC to MIBC: a bioinformatics analysis study

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Heng Zhang ◽  
Gang Shan ◽  
Jukun Song ◽  
Ye Tian ◽  
Ling-Yue An ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Bladder Cancer ◽  
Signaling Pathway ◽  
Kegg Pathway ◽  
Invasive Bladder Cancer ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive

Abstract Bladder cancer is the 11th most common cancer in the world. Bladder cancer can be roughly divided into muscle invasive bladder cancer (MIBC) and non-muscle invasive bladder cancer (NMIBC). The aim of the present study was to identify the key genes and pathways associated with the progression of NMIBC to MIBC and to further analyze its molecular mechanism and prognostic significance. We analyzed microarray data of NMIBC and MIBC gene expression datasets (GSE31684) listed in the Gene Expression Omnibus (GEO) database. After the dataset was analyzed using R software, differentially expressed genes (DEGs) of NMIBC and MIBC were identified. These DEGs were analyzed using Gene Ontology (GO) enrichment, KOBAS-Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein–protein interaction (PPI) analysis. The effect of these hub genes on the survival of bladder cancer patients was analyzed in The Cancer Genome Atlas (TCGA) database. A total of 389 DEGs were obtained, of which 270 were up-regulated and 119 down-regulated. GO and KEGG pathway enrichment analysis revealed that DEGs were mainly involved in the pathway of protein digestion and absorption, extracellular matrix (ECM) receiver interaction, phantom, toll-like receptor (TLR) signaling pathway, focal adhesion, NF-κB signaling pathway, PI3K/Akt signaling pathway, and other signaling pathways. Top five hub genes COL1A2, COL3A1, COL5A1, POSTN, and COL12A1 may be involved in the development of MIBC. These results may provide us with a further understanding of the occurrence and development of MIBC, as well as new targets for the diagnosis and treatment of MIBC in the future.

MYLK, CNN1, TAGLN and LMOD1 identified as potential prognostic biomarkers for bladder cancer using bioinformatics analysis

2021 ◽  
Author(s):  
zhiyong tan ◽  
Xuhua Qiao ◽  
Shi Fu ◽  
Xianzhong Duan ◽  
Yigang Zuo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Bladder Cancer ◽  
Signaling Pathway ◽  
Kegg Pathway ◽  
Mapk Signaling ◽  
Prognostic Biomarkers ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Bladder cancer (BCa) is a challenge carcinoma that occurs on the bladder mucosa, which is the most common malignant neoplasm of the urinary system. Great efforts have been made to elucidate its pathogenesis. However, the molecular mechanisms involved in BCa remain unclear. Therefore, there is an urgent need to identify effective biomarkers to accurately predict the progression and prognosis of BCa.Material and methods: To investigate potential prognostic biomarkers of BCa, we download the GSE23732 expression profile from Gene Expression Omnibus (GEO) database. The GEO2R analysis tool was performed to identify the DEGs between BCa and normal bladder mucosae tissue. Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the screened DEGs by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool. We employed the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct the protein-protein interaction (PPI) network of DEGs. Subsequently, the PPI network’s information was visualized by Cytoscape software. The Gene Expression Profiling Interactive Analysis (GEPIA) resource was used to describe the OS and DFS outcomes in bladder cancer patients based on the hub genes expression levels.Results: A total of 396 DEGs comprising 344 upregulated genes and 52 downregulated genes were screened. The results of the GO analysis showed that DEG was mainly enriched in proteinaceous extracellular matrix, extracellular matrix, heparin binding and extracellular matrix organization. In addition, KEGG pathway analysis showed that DEGs were mainly enriched in PI3K-Akt signaling pathway, Focal adhesion, MAPK signaling pathway. A PPI network was constructed using the 396 DEGs, 10 hub genes were selected and 4 of them including MYLK, CNN1, TAGLN and LMOD1 were associated with overall survival and disease-free survival.Conclusion: MYLK, CNN1, TAGLN and LMOD1 may represent promising prognostic biomarkers and potential therapeutic option for BCa.

scholarly journals Gene Expression Analyses in Non Muscle Invasive Bladder Cancer Reveals a Role for Alternative Splicing and Tp53 Status

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Marta Dueñas ◽  
Andrés Pérez-Figueroa ◽  
Carla Oliveira ◽  
Cristian Suárez-Cabrera ◽  
Abel Sousa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Alternative Splicing ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Gene Expression Analyses ◽  
Muscle Invasive ◽  
Tp53 Status

scholarly journals Machine learning prediction models based on hub genes related to immune phenotypes in muscle-invasive bladder cancer treated with immune checkpoint blockade

2021 ◽  
Author(s):  
Peiheng Li ◽  
Zhi-Xin Chen ◽  
Dong Wang ◽  
Zhi Zheng ◽  
Zhi-Gang Ji
Keyword(s):  
Bladder Cancer ◽  
Real Time ◽  
Prediction Models ◽  
Prognostic Score ◽  
Invasive Bladder Cancer ◽  
Hub Genes ◽  
Muscle Invasive Bladder Cancer ◽  
Omics Analysis ◽  
Immune Phenotypes ◽  
Muscle Invasive

Abstract Background Bladder cancer is one of the most frequent cancers in the world. Muscle-invasive bladder cancer (MIBC) is the aggressive subtype with higher morbidity and mortality. Immune check point blockade (ICB) therapy has shown its potential for treating MIBC, but is limited due to the lack of predictive biomarkers. Methods 1601 MIBC transcriptomic profiles were obtained from 10 datasets. Unsupervised clustering of immune phenotypes in MIBC was performed based on immune-related signature genes selected by us. We analyzed the characteristics including microenvironments, metabolic pathways, and survival rates in different phenotypes. Multi-omics analysis and WGCNA plus protein-protein interaction (PPI) analysis were performed to identify hub genes of differentially expressed genes (DEGs) distinguishing phenotypes related to prognosis. The hub DEGs were further validated by real-time quantitative PCR (qPCR). A model was established and CART was employed to predict the responses of patients treated with ICB. Results Of various immune phenotypes, cluster 3C was the most “inflamed” subcluster with the best prognosis, while cluster 1A was associated with “non-inflammation” and worst prognosis. There was no intersection of hub DEGs selected by WGCNA plus PPI analysis and multi-omics analysis. WGCNA plus PPI analysis identified 5 hub genes related to the survival rate of patients, IFNG, CXCR6, IL2RB, LCK, and PSMB10. Real-time qPCR results indicated that the expression levels of 5 hub genes were significantly lower in tumors. The 5 hub genes were further utilized for prognostic score model and decision tree analysis. The areas under the curve (AUC) of the ROC curves predicting 5-year endpoint generated from the risk-based prediction model were 0.652. The mean accuracy, sensitivity, and specificity of CART for predicting stable disease/progressive disease were 70.1%, 70.0% and 71.7%. Conclusions The 5 hub genes and generated models showed the potential for predicting the prognosis for patients receiving ICB therapy. The molecular mechanisms regulating the expression of the hub genes require further studies in the future.

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