Gallbladder cancer (GBC) is the most common malignancy of the biliary system in clinic, which has the characteristics of insidious onset and high degree of malignancy. Most patients have progressed to an advanced stage when they are diagnosed. Early identification of risk factors of the onset of gallbladder cancer and active intervention are the key to improve the rate of early diagnosis and prognosis of gallbladder cancer. At present, the risk factors related to the onset of gallbladder cancer include gallstone, gallbladder polyps, primary sclerosing cholangitis, etc. In this review, we discuss the relevant latest research on the risk factors of the onset of gallbladder cancer in order to provide clinical evidence for the prevention and early diagnosis of gallbladder cancer. The intervention, follow-up, and monitoring of risk factors should be strengthened, and the possibility of malignancy of the gallbladder should be accurately assessed in combination with factors such as age and sex. In the case of possible malignancy, prophylactic cholecystectomy should be actively performed.
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.
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