CircPPAP2B controls metastasis of clear cell renal cell carcinoma via HNRNPC-dependent alternative splicing and targeting the miR-182-5p/CYP1B1 axis
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ABSTRACT:
SUBMITTER: Zheng Z
PROVIDER: S-EPMC10770969 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundRenal cell carcinoma (RCC) accounts for 90% of renal cancers, of which clear cell carcinoma (ccRCC) is the most usual histological type. The process of alternative splicing (AS) contributes to protein diversity, and the dysregulation of protein diversity may have a great influence on tumorigenesis. We developed a prognostic signature and comprehensively analyzed the role of tumor immune microenvironment (TIME) and immune checkpoint blocking (ICB) treatment in ccRCC.MethodsTo identify prognosis-related AS events, univariate Cox regression was used and functional annotation was performed using gene set enrichment analysis (GSEA). In this study, prognostic signatures were developed based on multivariate Cox, univariate Cox, and LASSO regression models. Moreover, to assess the prognostic value, the proportional hazards model, Kruskal-Wallis analysis, and ROC curves were used. To obtain a better understanding of TIME in ccRCC, the ESTIMATE R package, single sample gene set enrichment analysis (ssGSEA) algorithm, CIBERSORT method, and the tumor immune estimation resource (TIMER) were applied. The database was searched to verify the expression of C4OF19 in tumor and normal samples. Regulatory networks for AS-splicing factors (SFs) were visualized using Cytoscape 3.9.1.ResultsThere were 9,347 AS cases associated with the survival of ccRCC patients screened. A total of eight AS prognostic signatures were developed with stable prognostic predictive accuracy based on splicing subtypes. In addition, a qualitative prognostic nomogram was developed, and the prognostic prediction showed high effectiveness. In addition, we found that the combined signature was significantly associated with the diversity of TIME and ICB treatment-related genes. C4ORF19 might become an important prognostic factor for ccRCC. Finally, the AS-SF regulatory network was established to clearly reveal the potential function of SFs.ConclusionWe found novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.) for the prognostic prediction of ccRCC. A new and reliable prognostic nomogram was established to quantitatively predict the clinical outcome. The AS-SF networks could provide a new way for the study of potential regulatory mechanisms, and the important roles of AS events in the context of TIME and immunotherapy efficiency were exhibited. C4ORF19 was found to be a vital gene in TIME and ICB treatment.
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Project description:Abnormal alternative splicing (AS) caused by alterations in spliceosomal factors is implicated in cancers. Standard models posit that splice site selection is mainly determined by early spliceosomal U1 and U2 snRNPs. Whether and how other mid/late-acting spliceosome components such as USP39 modulate tumorigenic splice site choice remains largely elusive. We observed that hepatocyte-specific overexpression of USP39 promoted hepatocarcinogenesis and potently regulated splice site selection in transgenic mice. In human liver cancer cells, USP39 promoted tumor proliferation in a spliceosome-dependent manner. USP39 depletion deregulated hundreds of AS events, including the oncogenic splice-switching of KANK2. Mechanistically, we developed a novel RBP-motif enrichment analysis and found that USP39 modulated exon inclusion/exclusion by interacting with SRSF6/HNRNPC in both humans and mice. Our data represented a paradigm for the control of splice site selection by mid/late-acting spliceosome proteins and their interacting RBPs. USP39 and possibly other mid/late-acting spliceosome proteins may represent potential prognostic biomarkers and targets for cancer therapy.
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Project description:BackgroudCancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal the relationship between stemness and cancer. However, we found that existing methods do not perform well in assessing the stemness of KIRC patients, and they overlooked the impact of alternative splicing. Alternative splicing not only progresses during the differentiation of stem cells, but also changes during the acquisition of the stemness features of cancer stem cells. There is an urgent need for a new method to predict KIRC-specific stemness more accurately, so as to provide help in selecting treatment options.MethodsThe corresponding RNA-Seq data were obtained from the The Cancer Genome Atlas (TCGA) data portal. We also downloaded stem cell RNA sequence data from the Progenitor Cell Biology Consortium (PCBC) Synapse Portal. Independent validation sets with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database. we constructed a KIRC-specific stemness prediction model using an algorithm called one-class logistic regression based on the expression and alternative splicing data to predict stemness indices of KIRC patients, and the model was externally validated. We identify stemness-associated alternative splicing events (SASEs) by analyzing different alternative splicing event between high- and low- stemness groups. Univariate Cox and multivariable logistic regression analysisw as carried out to detect the prognosis-related SASEs respectively. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of our model.ResultsHere, we constructed a KIRC-specific stemness prediction model with an AUC of 0.968,and to provide a user-friendly interface of our model for KIRC stemness analysis, we have developed KIRC Stemness Calculator and Visualization (KSCV), hosted on the Shiny server, can most easily be accessed via web browser and the url https://jiang-lab.shinyapps.io/kscv/ . When applied to 605 KIRC patients, our stemness indices had a higher correlation with the gender, smoking history and metastasis of the patients than the previous stemness indices, and revealed intratumor heterogeneity at the stemness level. We identified 77 novel SASEs by dividing patients into high- and low- stemness groups with significantly different outcome and they had significant correlations with expression of 17 experimentally validated splicing factors. Both univariate and multivariate survival analysis demonstrated that SASEs closely correlated with the overall survival of patients.ConclusionsBasing on the stemness indices, we found that not only immune infiltration but also alternative splicing events showed significant different at the stemness level. More importantly, we highlight the critical role of these differential alternative splicing events in poor prognosis, and we believe in the potential for their further translation into targets for immunotherapy.
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Project description:BackgroundNon-small-cell lung cancer (NSCLC) is one of the major diseases that threaten human health, and there is still no fundamental treatment method. Emerging evidences suggested that circRNAs might be an effective target to treatment NSCLC. However, the roles and detailed mechanisms of hsa_circ_0003176 in NSCLC still not clear.Methodshsa_circ_0003176 was identified from GSE101684 and GSE112214 datasets of Gene Expression Omnibus (GEO) database. The expression of hsa_circ_0003176 was detected by RT-qPCR in NSCLC tissues, paired adjacent nontumor tissues, and cell lines. RNA fluorescence in situ hybridization and nuclear and cytoplasmic RNA fractionation analysis was used to detect the subcellular localization of hsa_circ_0003176 in H1299 and A549 cells. Dual-luciferase reporter and RNA pull-down assay were used to confirm the regulatory of miR-182-5p to hsa_circ_0003176 and RBM5. The roles of hsa_circ_0003176 in NSCLC progression was evaluated both in vitro by CCK-8 assay, colony formation assay, wound-healing assay, and matrigel transwell assay and in vivo by the subcutaneous xenograft nude mouse experiment and lung metastasis nude mouse experiment. In addition, RNA pull down and luciferase reporter assays were carried out to investigate the interaction between hsa_circ_0003176 or RBM5 and miR-182-5p.ResultsOur results indicated that hsa_circ_0003176 showed typical characteristic of circRNAs, which was downregulated in both NSCLC tissues and cell lines. Functionally, overexpression of hsa_circ_0003176 suppressed the proliferation, migration, and invasion of NSCLC cells in vitro and inhibited NSCLC growth and metastasis in vivo. Furthermore, we found that hsa_circ_0003176 acts as sponge of miR-182-5p to regulate RBM5 expression. Further, in vitro rescue experiments demonstrated that hsa_circ_0003176 suppressed the proliferation, migration, and invasion of NSCLC cells by regulating miR-182-5p/RBM5 axis.ConclusionWe demonstrated that hsa_circ_0003176 suppressed the NSCLC progression via regulating miR-182-5p/RBM5 axis. These data indicated that hsa_circ_0003176 might be a novel molecular target for NSCLC treatment.
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Project description:Alternative splicing (AS) contributes to protein diversity by modifying most gene transcriptions. Cancer generation and progression are associated with specific splicing events. However, AS signature in kidney renal clear cell carcinoma (KIRC) remains unknown. In this study, genome-wide AS profiles were generated in 537 patients with KIRC in the cancer genome atlas. With a total of 42 522 mRNA AS events in 10 600 genes acquired, 8164 AS events were significantly associated with the survival of patients with KIRC. Logistic regression analysis of the least absolute shrinkage and selection operator was conducted to identify an optimized multivariate prognostic predicting mode containing four predictors. In this model, the receptor-operator characteristic curves of the training set were built, and the areas under the curves (AUCs) at different times were >0.88, thus indicating a stable and powerful ability in distinguishing patients' outcome. Similarly, the AUCs of the test set at different times were >0.73, verifying the results of the training set. Correlation and gene ontology analyses revealed some potential functions of prognostic AS events. This study provided an optimized survival-predicting model and promising data resources for future in-depth studies on AS mechanisms in KIRC.
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