Hub RNA-binding Proteins Research Articles

Computational analysis of crosstalk between transcriptional regulators and RNA-binding proteins suggests mutual regulation of polycomb proteins and SRSF1 influencing adult hippocampal neurogenesis

BackgroundAdult hippocampal neurogenesis (AHN) is a clinically significant neural phenomenon. Understanding its molecular regulation would be important. In this regard, most studies have focused on transcriptional regulators (TRs), epigenetic modifiers, or non-coding RNAs. RNA-binding proteins (RBPs) have emerged as dominant molecular regulators. It would be significant to understand the potential cross-talk between RBPs and TRs, which could influence AHN.MethodsThe present study employed computational analyses to identify RBPs and TRs regulating AHN, followed by the analysis of their interaction networks and detection of hub proteins. Next, the potential mutual regulation of hub TRs and RBPs was analyzed. Additionally, hippocampal genes differentially expressed upon exercise were analyzed for potential regulation by the identified TRs and RBPs.Results105 TRs and 26 RBPs were found to influence AHN, which could also form interactive networks. Polycomb complex proteins were among the TR network hubs, while HNRNP and SRSF family members were among the hub RBPs. Further, the polycomb complex proteins and SRSF1 could have a mutual regulatory relationship, suggesting a cross-talk between epigenetic/transcriptional and post-transcriptional regulatory pathways. A number of exercise-induced hippocampal genes were also found to be potential targets of the identified TRs and RBPs.ConclusionSRSF1 may influence post-transcriptional stability, localization, and alternative splicing patterns of polycomb complex transcripts, and the polycomb proteins may in turn epigenetically influence the SRSF1. Further experimental validation of these regulatory loops/networks could provide novel insights into the molecular regulation of AHN, and unravel new targets for disease-treatment.

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.

Integration of three machine learning algorithms identifies characteristic RNA binding proteins linked with diagnosis, immunity and pyroptosis of IgA nephropathy.

Objective: RNA-binding proteins (RBPs) are essential for most post-transcriptional regulatory events, which exert critical roles in nearly all aspects of cell biology. Here, characteristic RBPs of IgA nephropathy were determined with multiple machine learning algorithms. Methods: Our study included three gene expression datasets of IgA nephropathy (GSE37460, GSE73953, GSE93798). Differential expression of RBPs between IgA nephropathy and normal samples was analyzed via limma, and hub RBPs were determined through MCODE. Afterwards, three machine learning algorithms (LASSO, SVM-RFE, random forest) were integrated to determine characteristic RBPs, which were verified in the Nephroseq database. Immune cell infiltrations were estimated through CIBERSORT. Utilizing ConsensusClusterPlus, IgA nephropathy were classified based on hub RBPs. The potential upstream miRNAs were predicted. Results: Among 388 RBPs with differential expression, 43 hub RBPs were determined. After integration of three machine learning algorithms, three characteristic RBPs were finally identified (DDX27, RCL1, and TFB2M). All of them were down-regulated in IgA nephropathy than normal specimens, with the excellent diagnostic efficacy. Additionally, they were significantly linked to immune cell infiltrations, immune checkpoints, and pyroptosis-relevant genes. Based on hub RBPs, IgA nephropathy was stably classified as two subtypes (cluster 1 and 2). Cluster 1 exhibited the relatively high expression of pyroptosis-relevant genes and characteristic RBPs. MiR-501-3p, miR-760, miR-502-3p, miR-1224-5p, and miR-107 were potential upstream miRNAs of hub RBPs. Conclusion: Collectively, our findings determine three characteristic RBPs in IgA nephropathy and two RBPs-based subtypes, and thus provide a certain basis for further research on the diagnosis and pathogenesis of IgA nephropathy.

Systematic Identification of the RNA-Binding Protein STAU2 as a Key Regulator of Pancreatic Adenocarcinoma

Simple SummaryPancreatic adenocarcinoma (PAAD) is one of the most common tumors of the gastrointestinal tract and is difficult to diagnose and treat due to tumor heterogeneity and the immunosuppressive tumor microenvironment. RNA-binding proteins have been studied and their dysregulation has been found to play a key role in altering RNA metabolism in various malignancies. STAU2 is one of them. To investigate the role of STAU2 in PAAD, we monitored the signaling pathway by regulating substrate mRNA and experimentally confirmed that STAU2 is the most potential biomarker for the occurrence and development of PAAD. Furthermore, we found that high expression of STAU2 not only contributes to immune evasion but also correlates with sensitivity to chemotherapeutic agents, suggesting that STAU2 may be a potential target for combined natural therapy. These results demonstrate that STAU2 is a novel prognostic and diagnostic biomarker for PAAD, revealing STAU2′s utility in cancer therapy and drug development.Pancreatic adenocarcinoma (PAAD) is a highly aggressive cancer. RNA-binding proteins (RBPs) regulate highly dynamic post-transcriptional processes and perform very important biological functions. Although over 1900 RBPs have been identified, most are considered markers of tumor progression, and further information on their general role in PAAD is not known. Here, we report a bioinformatics analysis that identified five hub RBPs and produced a high-value prognostic model based on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Among these, the prognostic signature of the double-stranded RNA binding protein Staufen double-stranded RNA (STAU2) was identified. Firstly, we found that it is a highly expressed critical regulator of PAAD associated with poor clinical outcomes. Accordingly, the knockdown of STAU2 led to a profound decrease in PAAD cell growth, migration, and invasion and induced apoptosis of PAAD cells. Furthermore, through multiple omics analyses, we identified the key target genes of STAU2: Palladin cytoskeletal associated protein (PALLD), Heterogeneous nuclear ribonucleoprotein U (HNRNPU), SERPINE1 mRNA Binding Protein 1 (SERBP1), and DEAD-box polypeptide 3, X-Linked (DDX3X). Finally, we found that a high expression level of STAU2 not only helps PAAD evade the immune response but is also related to chemotherapy drug sensitivity, which implies that STAU2 could serve as a potential target for combinatorial therapy. These findings uncovered a novel role for STAU2 in PAAD aggression and resistance, suggesting that it probably represents a novel therapeutic and drug development target.

Development of a 3 RNA Binding Protein Signature for Predicting Prognosis and Treatment Response for Glioblastoma Multiforme.

Purpose: Glioblastoma multiforme (GBM) is the most widely occurring brain malignancy. It is modulated by a variety of genes, and patients with GBM have a low survival ratio and an unsatisfactory treatment effect. The irregular regulation of RNA binding proteins (RBPs) is implicated in several malignant neoplasms and reported to exhibit an association with the occurrence and development of carcinoma. Thus, it is necessary to build a stable, multi-RBPs signature-originated model for GBM prognosis and treatment response prediction. Methods: Differentially expressed RBPs (DERBPs) were screened out based on the RBPs data of GBM and normal brain tissues from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Program (GTEx) datasets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses on DERBPs were performed, followed by an analysis of the Protein-Protein Interaction network. Survival analysis of the DERBPs was conducted by univariate and multivariate Cox regression. Then, a risk score model was created on the basis of the gene signatures in various survival-associated RBPs, and its prognostic and predictive values were evaluated through Kaplan-Meier analysis and log-rank test. A nomogram on the basis of the hub RBPs signature was applied to estimate GBM patients’ survival rates. Moreover, western blot was for the detection of the proteins. Results: BICC1, GNL3L, and KHDRBS2 were considered as prognosis-associated hub RBPs and then were applied in the construction of a prognostic model. Poor survival results appeared in GBM patients with a high-risk score. The area under the time-dependent ROC curve of the prognostic model was 0.723 in TCGA and 0.707 in Chinese Glioma Genome Atlas (CGGA) cohorts, indicating a good prognostic model. What was more, the survival duration of the high-risk group receiving radiotherapy or temozolomide chemotherapy was shorter than that of the low-risk group. The nomogram showed a great discriminating capacity for GBM, and western blot experiments demonstrated that the proteins of these 3 RBPs had different expressions in GBM cells. Conclusion: The identified 3 hub RBPs-derived risk score is effective in the prediction of GBM prognosis and treatment response, and benefits to the treatment of GBM patients.

The Function and Prognostic Value of RNA-Binding Proteins in Colorectal Adenocarcinoma Were Analyzed Based on Bioinformatics of Smart Medical Big Data.

Colon cancer is the third most frequent cancer in the world and is mainly adenocarcinoma in terms of pathological type. It has been confirmed that the dysregulation of RNA-binding proteins (RBPs) significantly participates in the occurrence and development of numerous malignant tumors. Therefore, we analyzed the RBPs associated with colon adenocarcinoma (COAD) to assess their possible biological effects and prognostic value. A total of 398 COAD tissue datasets and 39 normal tissue datasets were retrieved from the TCGA data resource and screened out the RBPs, which are differentially expressed between tumor tissues and nontumor tissues. Then, bioinformatics analyses based on smart medical big data were conducted on these RBPs. Overall, 181 differentially expressed RBPs were uncovered, consisting of 121 upregulated RBPs and 60 downregulated RBPs. Finally, we selected 7 prognostic-related RBPs with research prospects and constructed a prognostic model according to the median risk score. There were remarkable differences in OS between the high-risk and low-risk groups. In addition, the performance of the prognostic model was evaluated and verified with other COAD patient data in the TCGA database. The results showed that the area under the ROC curve (AUC) for the train group was 0.744 and the one for the test group was 0.661, confirming that the model assesses patients' prognosis to some extent. And based on 7 hub RBPs, we constructed a nomogram as a reference for evaluating the survival rate of COAD patients.

Integrated Analysis of Nine Prognostic RNA-Binding Proteins in Soft Tissue Sarcoma.

RNA-binding proteins (RBPs) have been shown to be dysregulated in cancer transcription and translation, but few studies have investigated their mechanism of action in soft tissue sarcoma (STS). Here, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to identify differentially expressed RBPs in STS and normal tissues. Through a series of biological information analyses, 329 differentially expressed RBPs were identified. Functional enrichment analysis showed that differentially expressed RBPs were mainly involved in RNA transport, RNA splicing, mRNA monitoring pathways, ribosome biogenesis and translation regulation. Through Cox regression analyses, 9 RBPs (BYSL, IGF2BP3, DNMT3B, TERT, CD3EAP, SRSF12, TLR7, TRIM21 and MEX3A) were all up-regulated in STS as prognosis-related genes, and a prognostic model was established. The model calculated a risk score based on the expression of 9 hub RBPs. The risk score could be used for risk stratification of patients and had a high prognostic value based on the receiver operating characteristic (ROC) curve. We also established a nomogram containing risk scores and 9 key RBPs to predict the 1-year, 3-year, and 5-year survival rates of patients in STS. Afterwards, methylation analysis showed significant changes in the methylation degree of BYSL, CD3EAP and MEX2A. Furthermore, the expression of 9 hub RBPs was closely related to immune infiltration rather than tumor purity. Based on the above studies, these findings may provide new insights into the pathogenesis of STS and will provide candidate biomarkers for the prognosis of STS.

Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer.

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

Development and validation of a prediction model for lung adenocarcinoma based on RNA-binding protein

BackgroundRNA-binding proteins (RBPs) have been found to participate in the development and progression of cancer. This present study aimed to construct a RBP-based prognostic prediction model for lung adenocarcinoma (LUAD).MethodsRNA sequencing data and corresponding clinical information were acquired from The Cancer Genome Atlas (TCGA) and served as a training set. The prediction model was validated using the dataset in Gene Expression Omnibus (GEO) databases. Univariate and multivariate Cox regression analyses were conducted to identify the RBPs associated with survival. R software (http://www.r-project.org) was used for analysis in this study.ResultsNine hub prognostic RBPs (CIRBP, DARS2, DDX24, GAPDH, LARP6, SNRPE, WDR3, ZC3H12C, ZC3H12D) were identified by univariate Cox regression analysis and multivariate Cox regression analysis. Using a risk score based on the nine-hub RBP model, we separated the LUAD patients into a low-risk group and a high-risk group. The outcomes revealed that patients in the high-risk group had poorer survival than those in the low-risk group. This signature was validated in the GEO database. Further study revealed that the risk score can be an independent prognostic biomarker for LUAD. A nomogram based on the nine hub RBPs was built to quantitatively predict the prognosis of LUAD patients.ConclusionsOur nine-gene signature model could be used as a marker to predict the prognosis of LUAD and has potential for use in treatment individualization.