Background Heterogeneous nuclear ribonucleoprotein F (hnRNP-F) has been implicated in multiple cancers, suggesting its role in tumourigenesis, but the potential oncogenic role and mechanism of hnRNP-F in bladder cancer (BC) remain incompletely understood. Methods HnRNP-F was identified by proteomic methods. A correlation of hnRNP-F expression with prognosis was analysed in 103 BC patients. Then, we applied in vitro and in vivo methods to reveal the behaviours of hnRNP-F in BC tumourigenesis. Furthermore, the interaction between hnRNP-F and Snail1 mRNA was examined by RNA immunoprecipitation (RIP), and Snail1 mRNA stability was measured after treatment with actinomycin D. Finally, the binding domain between hnRNP-F and Snail1 mRNA was verified by constructing Snail1 mRNA truncations and mutants. Finding HnRNP-F is significantly upregulated in BC tissue, and its increased expression is associated with a poor prognosis in BC patients. HnRNP-F is necessary for tumour growth, inducing epithelial-mesenchymal transition (EMT) and metastasis in BC. The changes in Snail1 expression were positively correlated with hnRNP-F at both the mRNA and protein levels when hnRNP-F was silenced or enhanced, suggesting that Snail1 is likely a downstream target of hnRNP-F that mediates its effects on enhancing invasion, metastasis and EMT in BC. The overexpression of hnRNP-F caused an increase in the stability of Snail1 mRNA. Our RNA chip analysis revealed that hnRNP-F could combine with Snail1 mRNA, and we further demonstrated that hnRNP-F could directly bind to the 3′ untranslated region (3′ UTR) of Snail1 mRNA to enhance its stability. Interpretation Our findings suggest that hnRNP-F mediates the stabilization of Snail1 mRNA by binding to its 3′ UTR, subsequently regulating EMT.
BackgroundImmune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.MethodsOpen datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.ResultsA total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.ConclusionTNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.
Background. Bladder cancer (BLCA) is one of the most common cancers and ranks ninth among all cancers. Extracellular matrix (ECM) genes activate a number of pathways that facilitate tumor development. This study is aimed at providing models to predict BLCA survival and recurrence by ECM genes. Methods. Expression data from BLCA samples in GSE32894, GSE13507, GSE31684, GSE32548, and TCGA-BLCA cohorts were downloaded and analyzed. The ECM-related genes were obtained by differentially expressed gene analysis, stage-associated gene analysis, and random forest variable selection. The ECM was constructed in GSE32894 by the hub ECM-related genes and validated in GSE13507, GSE31684, GSE32548, and TCGA-BLCA cohorts. The correlations of the ECM score with cells (T cells, fibroblasts, etc.) and the response to immunotherapeutic drugs were investigated. Four machine learning models were selected and used to construct models to predict the recurrence of BLCA. A total of 15 paired BLCA and normal tissue specimens, human immortalized uroepithelial cell lines, and bladder cancer cell lines were selected for the validation of the difference in expression of FSTL1 between normal tissues and BLCA. Results. Six ECM genes (CTHRC1, MMP11, COL10A1, FSTL1, SULF1, and COL5A3) were recognized to be the hub ECM-related genes. The ECM score of each BLCA patient was calculated using these six selected ECM-related genes. BLCA patients with a high ECM score group had significantly lower overall survival rates than patients in the low ECM score group. We found that the ECM score was positively associated with immune cells and fibroblasts and negatively correlated with tumor purity. When treated with immunotherapy, BLCA patients with a high ECM score presented a high response rate and better prognosis. We also found that the combination of FSTL1, stage, age, and gender achieved an AUC value of 0.76 in predicting bladder cancer recurrence. Based on the RT-qPCR results of FSTL1 gene expression, there was an overall decrease in the mRNA expression of FSTL1 in cancer tissues compared to their adjacent normal tissues. Subsequent in vitro validation demonstrated that the FSTL1 expression was downregulated at the gene and protein level compared to that in SVH cells. Conclusion. Taken together, our results indicate that ECM-related genes correlate with immune cells, overall survival, and recurrence of BLCA. This study provides a machine learning model for predicting the survival and recurrence of BLCA patients.
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