Analysing a Novel RNA-Binding-Protein-Related Prognostic Signature Highly Expressed in Breast Cancer

Background. Breast cancer (BRCA) is one of the most common cancers and the leading cause of cancer-related death in women. RNA-binding proteins (RBPs) play an important role in the emergence and pathogenesis of tumors. The target RNAs of RBPs are very diverse; in addition to binding to mRNA, RBPs also bind to noncoding RNA. Noncoding RNA can cause secondary structures that can bind to RBPs and regulate multiple processes such as splicing, RNA modification, protein localization, and chromosomes remodeling, which can lead to tumor initiation, progression, and invasion. Methods. (1) BRCA data were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases and were used as training and testing datasets, respectively. (2) The prognostic RBPs-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. (3) Univariate Cox proportional hazard regression was performed to identify the genes with significant prognostic value. (4) Further, we used the LASSO regression to construct a prognostic signature and validated the signature in the TCGA and ICGC cohort. (5) Besides, we also performed prognostic analysis, expression level verification, immune cell correlation analysis, and drug correlation analysis of the genes in the model. Results. Four genes (MRPL13, IGF2BP1, BRCA1, and MAEL) were identified as prognostic gene signatures. The prognostic model has been validated in the TCGA and ICGC cohorts. The risk score calculated with four genes signatures could largely predict overall survival for 1, 3, and 5 years in patients with BRCA. The calibration plot demonstrated outstanding consistency between the prediction and actual observation. The findings of online database verification revealed that these four genes were significantly highly expressed in tumors. Also, we observed their significant correlations with some immune cells and also potential correlations with some drugs. Conclusion. We constructed a 4-RBPs-based prognostic signature to predict the prognosis of BRCA patients, and it has the potential for treating and diagnosing BRCA.

Download Full-text

PIWIL4 and SUPT5H combine to predict prognosis and immune landscape in intrahepatic cholangiocarcinoma

Cancer Cell International ◽

10.1186/s12935-021-02310-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Wenbo Zou ◽

Zizheng Wang ◽

Xiuping Zhang ◽

Shuai Xu ◽

Fei Wang ◽

...

Keyword(s):

Intrahepatic Cholangiocarcinoma ◽

Real World ◽

Rna Binding ◽

Rna Binding Proteins ◽

Primary Liver Cancer ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Term Survival ◽

Qrt Pcr

Abstract Background Intrahepatic cholangiocarcinoma (ICC) is a fatal primary liver cancer, and its long-term survival rate remains poor. RNA-binding proteins (RBPs) play an important role in critical cellular processes, failure of any one or more processes can lead to the development of multiple cancers. This study aimed to explore pivotal biomarkers and corresponding mechanisms to predict the prognosis of patients with ICC. Methods The transcriptomic and clinical information of patients were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Bioinformatic methods were used to identify survival-related and differentially-expressed biomarkers. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry were used to detect the expression levels of key biomarkers in independent real-world cohorts. Subsequently, a prognostic signature was constructed that effectively distinguished patients in the high- and low-risk groups. Independent prognosis analysis was used to verify the signature’s independent predictive capabilities, and two nomograms were developed to predict survival. Results PIWIL4 and SUPT5H were identified and considered as pivotal biomarkers, and the same expression trends of upregulation in ICC were also validated via qRT-PCR and immunohistochemistry in the separate real-world sample cohorts. The prognostic signature showed good predictive capabilities according to the area under the curve. The correlation of the biomarkers with the tumour microenvironment suggested that the high riskScore was positively related to the enrichment of resting natural killer cells and activated memory CD4 + T cells. Conclusion In the present study, we demonstrated that PIWIL4 and SUPT5H could be used as novel prognostic biomarkers to develop a prognostic signature. This study provides potential biomarkers of prognostic value for patients with intrahepatic cholangiocarcinoma.

Download Full-text

Identification and Validation of a Novel RNA-Binding Protein-Related Gene-Based Prognostic Model for Multiple Myeloma

Frontiers in Genetics ◽

10.3389/fgene.2021.665173 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wei Wang ◽

Shi-wen Xu ◽

Xia-yin Zhu ◽

Qun-yi Guo ◽

Min Zhu ◽

...

Keyword(s):

Multiple Myeloma ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Prognostic Significance ◽

Staging System ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Major Pathway ◽

Risk Status

BackgroundMultiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. RNA-binding proteins (RBPs) are involved in the development of many tumors, but their prognostic significance has not been systematically described in MM. Here, we developed a prognostic signature based on eight RBP-related genes to distinguish MM cohorts with different prognoses.MethodAfter screening the differentially expressed RBPs, univariate Cox regression was performed to evaluate the prognostic relevance of each gene using The Cancer Genome Atlas (TCGA)-Multiple Myeloma Research Foundation (MMRF) dataset. Lasso and stepwise Cox regressions were used to establish a risk prediction model through the training set, and they were validated in three Gene Expression Omnibus (GEO) datasets. We developed a signature based on eight RBP-related genes, which could classify MM patients into high- and low-score groups. The predictive ability was evaluated using bioinformatics methods. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and gene set enrichment analyses were performed to identify potentially significant biological processes (BPs) in MM.ResultThe prognostic signature performed well in the TCGA-MMRF dataset. The signature includes eight hub genes: HNRNPC, RPLP2, SNRPB, EXOSC8, RARS2, MRPS31, ZC3H6, and DROSHA. Kaplan–Meier survival curves showed that the prognosis of the risk status showed significant differences. A nomogram was constructed with age; B2M, LDH, and ALB levels; and risk status as prognostic parameters. Receiver operating characteristic (ROC) curve, C-index, calibration analysis, and decision curve analysis (DCA) showed that the risk module and nomogram performed well in 1, 3, 5, and 7-year overall survival (OS). Functional analysis suggested that the spliceosome pathway may be a major pathway by which RBPs are involved in myeloma development. Moreover, our signature can improve on the R-International Staging System (ISS)/ISS scoring system (especially for stage II), which may have guiding significance for the future.ConclusionWe constructed and verified the 8-RBP signature, which can effectively predict the prognosis of myeloma patients, and suggested that RBPs are promising biomarkers for MM.

Download Full-text

Prognostic Value and Immunological Characteristics of a Novel RNA Binding Protein Signature in Cutaneous Melanoma

Frontiers in Genetics ◽

10.3389/fgene.2021.723796 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jun Tian ◽

Chongzhi Ma ◽

Li Yang ◽

Yang Sun ◽

Yuan Zhang

Keyword(s):

High Risk ◽

Cutaneous Melanoma ◽

Rna Binding ◽

Rna Binding Proteins ◽

Risk Group ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Potential Biomarker ◽

Melanoma Patients

BackgroundThe existing studies indicate that RNA binding proteins (RBPs) are closely correlated with the genesis and development of cancers. However, the role of RBPs in cutaneous melanoma remains largely unknown. Therefore, the present study aims to establish a reliable prognostic signature based on RBPs to distinguish cutaneous melanoma patients with different prognoses and investigate the immune infiltration of patients.MethodsAfter screening RBPs from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were then used to establish a prediction model. The relationship between the signature and the abundance of immune cell types, the tumor microenvironment (TME), immune-related pathways, and immune checkpoints were also analyzed.ResultsIn total, 7 RBPs were selected to establish the prognostic signature. Patients categorized as a high-risk group demonstrated worse overall survival (OS) rates compared to those of patients categorized as a low-risk group. The signature was validated in an independent external cohort and indicated a promising prognostic ability. Further analysis indicated that the signature wasan independent prognostic indicator in cutaneous melanoma. A nomogram combining risk score and clinicopathological features was then established to evaluate the 3- and 5-year OS in cutaneous melanoma patients. Analyses of immune infiltrating, the TME, immune checkpoint, and drug susceptibility revealed significant differences between the two groups. GSEA analysis revealed that basal cell carcinoma, notch signaling pathway, melanogenesis pathways were enriched in the high-risk group, resulting in poor OS.ConclusionWe established and validated a robust 7-RBP signature that could be a potential biomarker to predict the prognosis and immunotherapy response of cutaneous melanoma patients, which provides new insights into cutaneous melanoma immunotherapeutic strategies.

Download Full-text

Identification of an RNA binding protein-related gene signature in hepatocellular carcinoma patients

10.21203/rs.3.rs-86109/v1 ◽

2020 ◽

Author(s):

Li Wang ◽

Na Zhou ◽

Jialin Qu ◽

Man Jiang ◽

Xiaochun Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Binding Proteins ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Characteristic Curve ◽

Gene Signature ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters

Abstract Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality among all human cancers. Studies have demonstrated that RNA binding proteins (RBPs) involved in the biological process of cancers including hepatocellular cancer. In this study, we aim to identify clinical value of RNA binding proteins for hepatocellular carcinoma.Methods: We analyses the data of HCC that downloaded from the Cancer Genome Atlas (TCGA) database and determined the differently expressed of RBPs between cancer and normal tissues. We further elucidate the function of RBPs by utilized Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gene signature of SMG5, EZH2, FBLL1, ZNF239, IGF2BP3 were generated by performed the univariate and multivariate Cox regression and LASSO regression analysis. CIBERSORT analysis was used to evaluation of tumor-infiltrating immune cells in different group. We built and verify a prognosis nomogram base on RBPs-related genes. Gene signature was validated by the International Cancer Genome Consortium (ICGC) database. The expressions of RBPs-related genes were validated by using Oncomine database, and the Human Protein Atlas.Result: Most of RBPs-related genes were significantly different in cancer and normal tissue. The survival of patients in the different group was statistically different. The Gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters (AUC=0.758). The patients in the high-risk group were more likely to have a higher Macrophages M0. Conclusion: Gene signature constructed by RNA binding proteins can be independent risk factors for hepatocellular carcinoma patients.

Download Full-text

In silico analyses reveal new putative Breast Cancer RNA-binding proteins

10.1101/2020.01.08.898965 ◽

2020 ◽

Cited By ~ 5

Author(s):

Santiago Guerrero ◽

Andres Lopez-Cortes ◽

Jennyfer M. Garcia-Cardenas ◽

Isaac Armendariz-Castillo ◽

Ana Karina Zambrano ◽

...

Keyword(s):

Breast Cancer ◽

Drug Development ◽

In Silico ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Tumor Biology ◽

The Cancer Genome Atlas ◽

Cancer Drug ◽

In Silico Analyses

Breast cancer (BC) is the leading cause of cancer-associated death among women worldwide. Despite treatment efforts, advanced BC with distant organ metastases is considered incurable. A better understanding of BC molecular processes is therefore of great interest to identify new therapeutic targets. Although large-scale efforts, such as The Cancer Genome Atlas (TCGA), have completely redefined cancer drug development, diagnosis, and treatment, additional key aspects of tumor biology remain to be discovered. In that respect, post-transcriptional regulation of tumorigenesis represents an understudied aspect of cancer research. As key regulators of this process, RNA-binding proteins (RBPs) are emerging as critical modulators of tumorigenesis but only few have defined roles in BC. To unravel new putative BC RBPs, we have performed in silico analyses of all human RBPs in three major cancer databases (TCGA-Breast Invasive Carcinoma, the Human Protein Atlas, and the Cancer Dependency Map project) along with complementary bioinformatics resources (STRING protein-protein interactions and the Network of Cancer Genes 6.0). Thus, we have identified six putative BC progressors (MRPL13, SCAMP3, CDC5L, DARS2, PUF60, and PLEC), and five BC suppressors RBPs (SUPT6H, MEX3C, UPF1, CNOT1, and TNKS1BP1). These proteins have never been studied in BC but show similar cancer-associated features than well-known BC proteins. Further research should focus on the mechanisms by which these proteins promote or suppress breast tumorigenesis, holding the promise of new therapeutic pathways along with novel drug development strategies.

Download Full-text

Long noncoding RNA HOTAIR interacts with Y-Box Protein-1 (YBX1) to regulate cell proliferation

Life Science Alliance ◽

10.26508/lsa.202101139 ◽

2021 ◽

Vol 4 (9) ◽

pp. e202101139

Author(s):

Siting Li ◽

Qian Xiong ◽

Minghai Chen ◽

Bing Wang ◽

Xue Yang ◽

...

Keyword(s):

Cell Proliferation ◽

Protein Interaction ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Phosphoenolpyruvate Carboxykinase ◽

Rna Binding ◽

Fluorescent Protein ◽

Nuclear Translocation ◽

Rna Binding Proteins ◽

Protein Localization

HOTAIR is a long noncoding RNA (lncRNA) which serves as an important factor regulating diverse processes linked with cancer development. Here, we used comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS) to explore the HOTAIR-protein interactome. We were able to identify 348 proteins interacting with HOTAIR, allowing us to establish a heavily interconnected HOTAIR-protein interaction network. We further developed a novel near-infrared fluorescent protein (iRFP)-trimolecular fluorescence complementation (TriFC) system to assess the interaction between HOTAIR and its interacting proteins. Then, we determined that HOTAIR specifically binds to YBX1, promotes YBX1 nuclear translocation, and stimulates the PI3K/Akt and ERK/RSK signaling pathways. We further demonstrated that HOTAIR exerts its effects on cell proliferation, at least in part, through the regulation of two YBX1 downstream targets phosphoenolpyruvate carboxykinase 2 (PCK2) and platelet derived growth factor receptor β. Our findings revealed a novel mechanism, whereby an lncRNA is able to regulate cell proliferation via altering intracellular protein localization. Moreover, the imaging tools developed herein have excellent potential for future in vivo imaging of lncRNA–protein interaction.

Download Full-text

RNA-Binding Protein Dnd1 Promotes Breast Cancer Apoptosis by Stabilizing the Bim mRNA in a miR-221 Binding Site

BioMed Research International ◽

10.1155/2017/9596152 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Feng Cheng ◽

Ying Pan ◽

Yi-Min Lu ◽

Lei Zhu ◽

Shuzheng Chen

Keyword(s):

Breast Cancer ◽

Germ Cell ◽

Binding Site ◽

Rna Binding ◽

Rna Binding Proteins ◽

Expression Profiles ◽

Germ Cell Tumors ◽

Vital Role ◽

The Cancer Genome Atlas ◽

Mcf 7

RNA-binding proteins (RBPs) and miRNAs are capable of controlling processes in normal development and cancer. Both of them could determine RNA transcripts fate from synthesis to decay. One such RBP, Dead end (Dnd1), is essential for regulating germ-cell viability and suppresses the germ-cell tumors development, yet how it exerts its functions in breast cancer has remained unresolved. The level of Dnd1 was detected in 21 cancerous tissues paired with neighboring normal tissues by qRT-PCR. We further annotated TCGA (The Cancer Genome Atlas) mRNA expression profiles and found that the expression of Dnd1 and Bim is positively correlated (p=0.04). Patients with higher Dnd1 expression level had longer overall survival (p=0.0014) by KM Plotter tool. Dnd1 knockdown in MCF-7 cells decreased Bim expression levels and inhibited apoptosis. While knockdown of Dnd1 promoted the decay of Bim mRNA 3′UTR, the stability of Bim-5′UTR was not affected. In addition, mutation of miR-221-binding site in Bim-3′UTR canceled the effect of Dnd1 on Bim mRNA. Knockdown of Dnd1 in MCF-7 cells confirmed that Dnd1 antagonized miR-221-inhibitory effects on Bim expression. Overall, our findings indicate that Dnd1 facilitates apoptosis by increasing the expression of Bim via its competitive combining with miR-221 in Bim-3′UTR. The new function of Dnd1 may contribute to a vital role in breast cancer development.

Download Full-text

The Therapeutic Potential and Role of miRNA, lncRNA, and circRNA in Osteoarthritis

Current Gene Therapy ◽

10.2174/1566523219666190716092203 ◽

2019 ◽

Vol 19 (4) ◽

pp. 255-263 ◽

Cited By ~ 13

Author(s):

Yuangang Wu ◽

Xiaoxi Lu ◽

Bin Shen ◽

Yi Zeng

Keyword(s):

Gene Expression ◽

Gene Therapy ◽

Extracellular Matrix ◽

Inflammatory Reaction ◽

Noncoding Rna ◽

Rna Binding ◽

Rna Binding Proteins ◽

Protein Translation ◽

Cochrane Library

Background: Osteoarthritis (OA) is a disease characterized by progressive degeneration, joint hyperplasia, narrowing of joint spaces, and extracellular matrix metabolism. Recent studies have shown that the pathogenesis of OA may be related to non-coding RNA, and its pathological mechanism may be an effective way to reduce OA. Objective: The purpose of this review was to investigate the recent progress of miRNA, long noncoding RNA (lncRNA) and circular RNA (circRNA) in gene therapy of OA, discussing the effects of this RNA on gene expression, inflammatory reaction, apoptosis and extracellular matrix in OA. Methods: The following electronic databases were searched, including PubMed, EMBASE, Web of Science, and the Cochrane Library, for published studies involving the miRNA, lncRNA, and circRNA in OA. The outcomes included the gene expression, inflammatory reaction, apoptosis, and extracellular matrix. Results and Discussion: With the development of technology, miRNA, lncRNA, and circRNA have been found in many diseases. More importantly, recent studies have found that RNA interacts with RNA-binding proteins to regulate gene transcription and protein translation, and is involved in various pathological processes of OA, thus becoming a potential therapy for OA. Conclusion: In this paper, we briefly introduced the role of miRNA, lncRNA, and circRNA in the occurrence and development of OA and as a new target for gene therapy.

Download Full-text

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

Current Bioinformatics ◽

10.2174/1574893615666210108093950 ◽

2021 ◽

Vol 15 ◽

Author(s):

Lichao Zhang ◽

Zihong Huang ◽

Liang Kong

Keyword(s):

Computational Model ◽

Binding Sites ◽

Noncoding Rna ◽

Computational Models ◽

Information Sources ◽

Rna Binding ◽

Rna Binding Proteins ◽

Gradient Boosting ◽

Position Information ◽

Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

Download Full-text