Development and validation of a prognostic model for kidney renal clear cell carcinoma based on RNA binding protein expression

Deubiquitinating enzyme OTUD4 stabilizes RBM47 to induce ATF3 transcription: a novel mechanism underlying the restrained malignant properties of ccRCC cells

Dysregulation of deubiquitination contributes to various diseases, including cancer, and aberrant expression of deubiquitinating enzymes is involved in carcinoma progression. As a member of the ovarian tumor (OTU) deubiquitinases, OTUD4 is considered a tumor suppressor in many kinds of malignancies. The biological characteristics and mechanisms of OTUD4 in clear cell renal cell carcinoma (ccRCC) remain unclear. The downregulation of OTUD4 in ccRCC was confirmed based on the TCGA database and a validation cohort of 30-paired ccRCC and para-carcinoma samples. Moreover, OTUD4 expression was detected by immunohistochemistry in 50 cases of ccRCC tissues, and patients with lower levels of OTUD4 showed larger tumor size (p = 0.015). TCGA data revealed that patients with high expression of OTUD4 had a longer overall survival rate. In vitro and in vivo studies revealed that downregulation of OTUD4 was essential for tumor cell growth and metastasis in ccRCC, and OTUD4 overexpression inhibited these malignant phenotypes. We further found that OTUD4 sensitized ccRCC cells to Erastin-induced ferroptosis, and ferrostain-1 inhibited OTUD4-induced ferroptotic cell death. Mechanistic studies indicated that OTUD4 functioned as an anti-proliferative and anti-metastasic factor through the regulation of RNA-binding protein 47 (RBM47)-mediated activating transcription factor 3 (ATF3). OTUD4 directly interacted with RBM47 and promoted its stability via deubiquitination events. RBM47 was critical in ccRCC progression by regulating ATF3 mRNA stability, thereby promoting ATF3-mediated ferroptosis. RBM47 interference abolished the suppressive role of OTUD4 overexpression in ccRCC. Our findings provide mechanistic insight into OTUD4 of ccRCC progression and indicate a novel critical pathway OTUD4/RBM47/ATF3 may serve as a potential therapeutic pathway for ccRCC.

Identification of Regulatory RNA-Binding Proteins Associated with Immune Infiltration in Laryngeal Squamous Cell Carcinoma

We analyzed bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to identify alternative splicing (AS) events and regulatory RNA-binding proteins (RBPs) associated with immune infiltration in human laryngeal squamous cell carcinoma (LSCC). Whole-transcriptome sequencing data of 20 human laryngeal cancer and paracancerous tissues were downloaded from the Gene Expression Omnibus public database, using newly published splicing-site usage variation analysis software to obtain highly conserved regulated AS (RAS) events, and scientific reverse convolution algorithm analysis was used to identify significantly different immune cells and perform a correlation analysis between the two. The software package edgeR was used to identify differentially expressed RBPs and the immune infiltration-related LSCC-RAS they may regulate. Finally, we present the expression profiles and survival curves of 117 human laryngeal cancer samples from The Cancer Genome Atlas dataset for the identified RBPs and LSCC-RAS. We also downloaded the gene set enrichment 150321 scRNA-seq data for two human LSCC tissue samples. The RBP expression pattern and the expression of prophase RBP genes were analyzed in different LSCC cell populations. RNA-binding motif protein 47 (RBM47) and filamin A, as well as the RBP-RAS events that were screened in both the fibulin 2 and fibronectin 1 genes, were all significantly associated with the prognosis, and the RBM47 gene was upregulated in myeloid cells. Because the prognosis was significantly associated with two RBP regulators and two LSCC-RAS events, they may be critical regulators of immune cell survival during laryngeal cancer progression, and RBM47 may regulate macrophage-associated AS and affect immunity.

Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.

Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.

Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.

Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.

Values of OAS gene family in the expression signature, immune cell infiltration and prognosis of human bladder cancer

Background

Bladder cancer (BLCA) is one of the most common genitourinary malignancies in the world, but its pathogenic genes have not been fully identified and the treatment outcomes are still unsatisfactory. Although the members of 2', 5'-oligoadenylate synthetase (OAS) gene family are known involved in some tumorous biological processes, the roles of the OAS gene family in BLCA are still undetermined.

Methods

By combining vast bioinformatic datasets analyses of BLCA and the experimental verification on clinical BLCA specimen, we identified the expressions and biological functions of OAS gene family members in BLCA with comparison to normal bladder tissues.

Results

The expression levels of OAS gene family members were higher in BLCA than in normal bladder tissues. The expression levels of most OAS genes had correlations with genomic mutation and methylation, and with the infiltration levels of CD4 + T cells, CD8 + T cells, neutrophils, and dendritic cells in the microenvironment of BLCA. In addition, high expressions of OAS1, OAS2, OAS3, and OASL predicted better overall survival in BLCA patients.

Conclusions

The highly expressed OAS genes in BLCA can reflect immune cells infiltration in the tumor microenvironment and predict the better overall survival of BLCA, and thus may be considered as a signature of BLCA. The study provides new insights into the diagnosis, treatment, and prognosis of BLCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10102-8.

A novel immune-related RNA-binding proteins signature to predict survival and therapeutic responses in prostate cancer

BACKGROUND

The present study aims to identify immune-related RBPs signature to predict prognosis and therapy response in prostate cancer.

METHODS

Differentially expressed RBPs were compared and visualized using R packages. Immune-related RBPs were selected by Pearson correlation analysis. The prognostic immune-related RBPs were identified using the Kaplan-Meier method and LASSO regression. A multivariable Cox regression model was used to construct immune-related RBPs signature.

RESULTS

We constructed a prognostic predictive risk model of prostate cancer containing ten immune-related RBP genes. We found that high-risk prostate cancer patients presented poorer prognosis, higher tumor immune cell infiltration, higher rates of genomic alterations, and were more sensitive to targeted and immunotherapy than the low-risk group.

CONCLUSIONS

The immune-related RBPs' signature is an independent prognostic marker that could help screen patients with advanced prostate cancer who are better suited for targeted and immunotherapy.

NOL12 as an Oncogenic Biomarker Promotes Hepatocellular Carcinoma Growth and Metastasis

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis worldwide. However, the pathogenesis of HCC remains poorly understood. In this study, we found that NOL12 was significantly overexpressed in independent HCC datasets from TCGA database. We confirmed that the expression level of NOL12 was upregulated in human HCC tissues and cell lines by RT-qPCR. High expression of NOL12 is associated with worse reduced overall survival (OS), high pathological grade, node metastasis, and advanced clinical stage in patients with HCC. Moreover, knockdown of NOL12 dramatically inhibits the proliferation and metastasis of HCC cells in vitro and in vivo. CIBERSORTx analysis revealed that twelve types of tumor-infiltrating immune cells (TICs) are correlated with NOL12 expression. The risk signature based on 8 NOL12-related genes is an independent prognostic factor for patients with HCC. The OS rate of patients in the low-risk score group was better than that in the high-risk score group. In addition, the total tumor mutation burden (TMB) in the high-risk score group increased significantly, and the risk scores could be used as an alternative indicator of immune checkpoint inhibitor (ICI) response. In conclusion, our findings indicated that NOL12 might be involved in the progression of HCC and can be used as a potential therapeutic target. Moreover, the NOL12-related risk signature may have predictive relevance with regard to ICI therapy.

Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma

BACKGROUND

The dysfunction of RNA binding proteins (RBPs) is associated with various inflammation and cancer. The occurrence and progression of tumors are closely related to the abnormal expression of RBPs. There are few studies on RBPs in clear cell renal carcinoma (ccRCC), which allows us to explore the role of RBPs in ccRCC.

METHODS

We obtained the gene expression data and clinical data of ccRCC from the Cancer Genome Atlas (TCGA) database and extracted all the information of RBPs. We performed differential expression analysis of RBPs. Risk model were constructed based on the differentially expressed RBPs (DERBPs). The expression levels of model markers were examined by reverse transcription-quantitative PCR (RT-qPCR) and analyzed for model-clinical relevance. Finally, we mapped the model's nomograms to predict the 1, 3 and 5-year survival rates for ccRCC patients.

RESULTS

The results showed that the five-year survival rate for the high-risk group was 40.2% (95% CI = 0.313 ~ 0.518), while the five-year survival rate for the low-risk group was 84.3% (95% CI = 0.767 ~ 0.926). The ROC curves (AUC = 0.748) also showed that our model had stable predictive power. Further RT-qPCR results were in accordance with our analysis (p < 0.05). The results of the independent prognostic analysis showed that the model could be an independent prognostic factor for ccRCC. The results of the correlation analysis also demonstrated the good predictive ability of the model.

CONCLUSION

In summary, the 4-RBPs (EZH2, RPL22L1, RNASE2, U2AF1L4) risk model could be used as a prognostic indicator of ccRCC. Our study provides a possibility for predicting the survival of ccRCC.

Identification and verification of YBX3 and its regulatory gene HEIH as an oncogenic system: A multidimensional analysis in colon cancer

The novel gene YBX3 is important for regulating translation and RNA catabolism and encodes a protein with a highly conserved cold-shock domain. However, its pathogenic roles across cancers (e.g., colon cancer) and its regulation remain unclear. We identified the pathogenic roles of YBX3 and its regulatory lncRNA HEIH in various cancers and investigated their effects on tumor progression in colon cancer. Methods including RNA pull-down, MS, and TMA of 93 patients, qPCR of 12 patients with diverse clinicopathologic stages, and western blotting were performed. The pancancer analysis showed that YBX3 expression varies significantly among not only cancer types but also molecular and immune subtypes of the same cancer. Furthermore, its expression in colon cancer is clinically significant, and there is an obvious negative regulatory association between HEIH and YBX3. Among various cancers, especially colon cancer, YBX3 is more related than HEIH expression to the clinical features and prognosis of subgroups. The receiver operating characteristic analysis showed that HEIH and YBX3 have similar predictive capacity in various cancers. The analysis of differentially expressed genes in colon cancer revealed that they have similar hub gene networks, indicating an oncogenic system with a strong overlap. The results also suggest that YBX3 is associated with tumor immune evasion via different mechanisms involving T-cell exclusion in different cancer types and by the tumor infiltration of immune cells. Interestingly, scRNA-seq revealed that HEIH inhibits this phenomenon. Our results also suggest that YBX3 expression is associated with immune or chemotherapeutic outcomes in various cancers, and YBX3 exhibited a higher predictive power than two of seven standardized biomarkers for response outcomes and overall survival of immune checkpoint blockade subcohorts. In colon cancer cell lines, lncRNA-HEIH and YBX3 associate. MS confirmed that YBX3 was pulled down with HEIH, and western blot showed that HEIH knockdown disinhibited YBX3. This study strongly suggests that lncRNA-HEIH/YBX3 is a pancancer immune-oncogenic system and could serve as a biomarker for diagnosis and prognosis and as a therapeutic target, especially in colon cancer.

RNA binding motif 47 (RBM47): emerging roles in vertebrate development, RNA editing and cancer

RNA-binding proteins (RBPs) are critical players in the post-transcriptional regulation of gene expression and are associated with each event in RNA metabolism. The term 'RNA-binding motif' (RBM) is assigned to novel RBPs with one or more RNA recognition motif (RRM) domains that are mainly involved in the nuclear processing of RNAs. RBM47 is a novel RBP conserved in vertebrates with three RRM domains whose contributions to various aspects of cellular functions are as yet emerging. Loss of RBM47 function affects head morphogenesis in zebrafish embryos and leads to perinatal lethality in mouse embryos, thereby assigning it to be an essential gene in early development of vertebrates. Its function as an essential cofactor for APOBEC1 in C to U RNA editing of several targets through substitution for A1CF in the A1CF-APOBEC1 editosome, established a new paradigm in the field. Recent advances in the understanding of its involvement in cancer progression assigned RBM47 to be a tumor suppressor that acts by inhibiting EMT and Wnt/[Formula: see text]-catenin signaling through post-transcriptional regulation. RBM47 is also required to maintain immune homeostasis, which adds another facet to its regulatory role in cellular functions. Here, we review the emerging roles of RBM47 in various biological contexts and discuss the current gaps in our knowledge alongside future perspectives for the field.

Systematic Analysis Identifies a Specific RNA-Binding Protein-Related Gene Model for Prognostication and Risk-Adjustment in HBV-Related Hepatocellular Carcinoma

Objective

Increasing evidence shows that dysregulated RNA binding proteins (RBPs) modulate the progression of several malignancies. Nevertheless, their clinical implications of RBPs in HBV-related hepatocellular carcinoma (HCC) remain largely undefined. Here, this study systematically analyzed the associations of RBPs with HBV-related HCC prognosis.

Methods

Based on differentially expressed RBPs between HBV-related HCC and control specimens, prognosis-related RBPs were screened by univariate analyses. A LASSO model was then created. Kaplan-Meier curves, ROCs, multivariate analyses, subgroup analyses and external verification were separately applied to assess the efficacy of this model in predicting prognosis and recurrence of patients. A nomogram was created by incorporating the model and clinical indicators, which was verified by ROCs, calibration curves and decision curve analyses. By CIBERSORT algorithm, the association between the risk score and immune cell infiltrations was evaluated.

Results

Totally, 54 RBPs were distinctly correlated to prognosis of HBV-related HCC. An 11-RBP model was created, containing POLR2L, MRPS12, DYNLL1, ZFP36, PPIH, RARS, SRP14, DDX41, EIF2B4, and NOL12. This risk score sensitively and accurately predicted one-, three- and five-year overall survival, disease-free survival, and progression-free interval. Compared to other clinical parameters, this risk score had the best predictive efficacy. Also, the clinical generalizability of the model was externally verified in the GSE14520 dataset. The nomogram may predict patients' survival probabilities. Also, the risk score was related to the components in the immune microenvironment.

Conclusion

Collectively, RBPs may act as critical elements in the malignant progression of HBV-related HCC and possess potential implications on prognostication and therapy decision.

Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis

Background

Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles.

Methods

Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters.

Results

GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients.

Conclusion

In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.

Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.