In recent years, the NOTCH signaling pathway has been gradually studied in human malignancies. Inactivation of the NOTCH signaling pathway was uncovered to be correlated with the carcinogenesis of bladder cancer (BCa). Nevertheless, the specific molecular mechanism of NOTCH1 (one of the core factors of the NOTCH signaling pathway) is not well elucidated in BCa. This study focused on the mechanism by which NOTCH1 affects the biological behaviors of BCa cells. According to the experimental results of quantitative real‐time polymerase chain reaction, NOTCH1 was dysregulated in BCa tissues and cell lines. The prognostic value of NOTCH1 for the patients with BCa was determined using the Kaplan‐Meier method. Mechanism investigations revealed that NOTCH1 is a target of miR‐34c‐5p in BCa. Furthermore, microarray analysis was used to find the dysregulated long noncoding RNAs (lncRNA), which can bind with miR‐34c‐5p. Mechanism experiments further demonstrated the rationality of the HCG18‐miR‐34c‐5p‐NOTCH1 pathway. Functional assays were then applied to validate the inhibitory influences of NOTCH1 on the proliferation and migration of BCa cells. Furthermore, the inhibitory effects of NOTCH1 could be affected by miR‐34c‐5p or lncRNA HCG18. All findings in this study revealed that NOTCH1 suppresses the BCa progression by cooperating with lncRNA HCG18 and miR‐34c‐5p.
Background: Renal cell carcinoma (RCC) is one of the most common cancers, with an annual incidence of nearly 400,000 cases worldwide. Increasing evidence has also demonstrated the vital role of neutrophil extracellular traps (NETs) in cancer progression and metastatic dissemination.Methods: Consensus cluster analysis was performed to determine the number of ccRCC subtypes. The Kruskal–Wallis test or Student t-test was performed to evaluate the difference of infiltrating immune cell and gene expression in different groups. The Kaplan–Meier (KM) method was used to draw the survival curve. LASSO cox regression analysis was conducted to construct a NET-related prognostic signature. We also constructed a lncRNA–miRNA–mRNA regulatory axis by several miRNA and lncRNA target databases.Results: A total of 23 differentially expressed NET-related genes were obtained in ccRCC. Three clusters of ccRCC cases with significant difference in prognosis, immune infiltration, and chemotherapy and targeted therapy were identified. LASSO Cox regression analysis identified a NET-related prognostic signature including six genes (G0S2, DYSF, MMP9, SLC22A4, SELP, and KCNJ15), and this signature had a good performance in predicting the overall survival of ccRCC patients. The expression of prognostic signature genes was significantly correlated with the pTMN stage, immune infiltration, tumor mutational burdens, microsatellite instability, and drug sensitivity of ccRCC patients. MMP9 was identified as the hub gene. We also identified the lncRNA UBA6-AS1/miR-149-5p/MMP9 regulatory axis for the progression of ccRCC.Conclusion: Collectively, the current study identified three molecular clusters and a prognostic signature for ccRCC based on neutrophil extracellular traps. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical transformation.
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