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

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.

scholarly journals Clinical characteristics and prognostic value of MEX3A mRNA in liver cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8252 ◽  
Author(s):  
Dingquan Yang ◽  
Yan Jiao ◽  
Yanqing Li ◽  
Xuedong Fang
Keyword(s):  
Liver Cancer ◽  
Clinical Data ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Diagnostic Ability ◽  
Kaplan Meier ◽  
Cox Analysis

Background MEX3A is an RNA-binding proteins (RBPs) that promotes the proliferation, invasion, migration and viability of cancer cells. The aim of this study was to explore the clinicopathological characteristics and prognostic significance of MEX3A mRNA expression in liver cancer. Methods RNA-Seq and clinical data were collected from The Cancer Genome Atlas (TCGA). Boxplots were used to represent discrete variables of MEX3A. Chi-square tests were used to analyze the correlation between clinical features and MEX3A expression. Receiver operating characteristic (ROC) curves were used to confirm diagnostic ability. Independent prognostic ability and values were assessed using Kaplan–Meier curves and Cox analysis. Results We acquired MEX3A RNA-Seq from 50 normal liver tissues and 373 liver cancer patients along with clinical data. We found that MEX3A was up-regulated in liver cancer which increased according to histological grade (p < 0.001). MEX3A showed moderate diagnostic ability for liver cancer (AUC = 0.837). Kaplan–Meier curves and Cox analysis revealed that the high expression of MEX3A was significantly associated with poor survival (OS and RFS) (p < 0.001). Moreover, MEX3A was identified as an independent prognostic factor of liver cancer (p < 0.001). Conclusions MEX3A expression shows promise as an independent predictor of liver cancer prognosis.

scholarly journals Integrated analysis of the functions and prognostic values of RNA binding proteins in colon adenocarcinoma

2020 ◽  
Author(s):  
Xinhong Liu ◽  
Fang Tan ◽  
Xingyao Long ◽  
Ruokun Yi ◽  
Dingyi Yang ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Prediction Models ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Cox Regression Analysis

Abstract Background RNA binding proteins (RBPs) play an important role in a variety of cancers. However, the role of RBPs in colorectal adenocarcinoma (COAD) has not been studied. Integrated analysis of RBPs will provide a better understanding of disease genesis and new insights into COAD treatment. Methods The gene expression data and corresponding clinical information for COAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was used to screen for RBPs associated with COAD recurrence, and multivariate Cox proportional hazards regression analyses were used to identify genes that were associated with COAD recurrence. A nomogram was constructed to predict the recurrence of COAD, and a receiver operating characteristic (ROC) curve analysis was performed to determine the accuracy of the prediction models. The Human Protein Atlas database was used in prediction models to confirm the expression of key genes in COAD patients. Result A total of 177 differentially expressed RBPs was obtained, comprising 123 upregulated and 54 downregulated. GO and KEGG enrichment analysis showed that the differentially expressed RBPs were mainly related to mRNA metabolism, RNA processing and translation regulation. Seven RBP genes (TDRD6, POP1, TDRD7, PPARGC1A, LIN28B, LRRFIP2 and PNLDC1) were identified as prognosis-associated genes and were used to construct the prognostic model. Conclusion We constructed a COAD prognostic model through bioinformatics analysis, which indicated that prognostic model RBPs have a potential role in the diagnosis and prognosis of COAD. Moreover, the nomogram can effectively predict the 1-year, 3-year, and 5-year survival rate for COAD patients.

scholarly journals The construction and validation of an RNA binding protein-related prognostic model for bladder cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fengxia Chen ◽  
Qingqing Wang ◽  
Yunfeng Zhou
Keyword(s):  
Bladder Cancer ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Sequence Data ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Depth Analysis

Abstract Background RNA-binding proteins (RBPs) play crucial and multifaceted roles in post-transcriptional regulation. While RBPs dysregulation is involved in tumorigenesis and progression, little is known about the role of RBPs in bladder cancer (BLCA) prognosis. This study aimed to establish a prognostic model based on the prognosis-related RBPs to predict the survival of BLCA patients. Methods We downloaded BLCA RNA sequence data from The Cancer Genome Atlas (TCGA) database and identified RBPs differentially expressed between tumour and normal tissues. Then, functional enrichment analysis of these differentially expressed RBPs was conducted. Independent prognosis-associated RBPs were identified by univariable and multivariable Cox regression analyses to construct a risk score model. Subsequently, Kaplan–Meier and receiver operating characteristic curves were plotted to assess the performance of this prognostic model. Finally, a nomogram was established followed by the validation of its prognostic value and expression of the hub RBPs. Results The 385 differentially expressed RBPs were identified included 218 and 167 upregulated and downregulated RBPs, respectively. The eight independent prognosis-associated RBPs (EFTUD2, GEMIN7, OAS1, APOBEC3H, TRIM71, DARS2, YTHDC1, and RBMS3) were then used to construct a prognostic prediction model. An in-depth analysis showed lower overall survival (OS) in patients in the high-risk subgroup compared to that in patients in the low-risk subgroup according to the prognostic model. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve were 0.795 and 0.669 for the TCGA training and test datasets, respectively, showing a moderate predictive discrimination of the prognostic model. A nomogram was established, which showed a favourable predictive value for the prognosis of BLCA. Conclusions We developed and validated the performance of a prognostic model for BLCA that might facilitate the development of new biomarkers for the prognostic assessment of BLCA patients.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yunyun Lan ◽  
Juan Su ◽  
Yaxin Xue ◽  
Lulu Zeng ◽  
Xun Cheng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Correlation Analysis ◽  
Noncoding Rna ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Localization ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Calibration Plot ◽  
Prognostic Signature

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.

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

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.

scholarly journals An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Shanshan Tang ◽  
Yiyi Zhuge
Keyword(s):  
Cancer Treatment ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Significance ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Multiple Cancer

Abstract Background Pseudogenes show multiple functions in various cancer types, and immunotherapy is a promising cancer treatment. Therefore, this study aims to identify immune-related pseudogene signature in endometrial cancer (EC). Methods Gene transcriptome data of EC tissues and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) through UCSC Xena browser. Spearman correlation analysis was performed to identify immune-related pseudogenes (IRPs) between the immune genes and pseudogenes. Univariate Cox regression, LASSO, and multivariate were performed to develop a risk score signature to investigate the different overall survival (OS) between high- and low-risk groups. The prognostic significance of the signature was assessed by the Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC) curve. The abundance of 22 immune cell subtypes of EC patients was evaluated using CIBERSORT. Results Nine IRPs were used to build a prognostic signature. Survival analysis revealed that patients in the low-risk group presented longer OS than those in the high-risk group as well as in multiple subgroups. The signature risk score was independent of other clinical covariates and was associated with several clinicopathological variables. The prognostic signature reflected infiltration by multiple types of immune cells and revealed the immunotherapy response of patients with anti-programmed death-1 (PD-1) and anti-programmed cell death 1 ligand 1 (PD-L1) therapy. Function enrichment analysis revealed that the nine IRPs were mainly involved in multiple cancer-related pathways. Conclusion We identified an immune-related pseudogene signature that was strongly correlated with the prognosis and immune response to EC. The signature might have important implications for improving the clinical survival of EC patients and provide new strategies for cancer treatment.

scholarly journals Systematic Analysis of the Functions and Prognostic Values of Rna Binding Protein in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Author(s):  
song jukun ◽  
Feng Liu ◽  
Bo Liu ◽  
Xianlin Cheng ◽  
Xinhai Yin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Head And Neck ◽  
Clinical Decision Making ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Clinical Decision ◽  
The Cancer Genome Atlas ◽  
Systematic Analysis ◽  
Cox Regression Analysis

Abstract Background: Dysregulation of RNA-binding proteins (RBPs) playsan important role in controlling processes in cancer development.However, the function of RBPs has not been thoroughly and systematically documented in head and neck cancer.We aim to explore the role of RPB in the pathogenesis of HNSC.Methods: We obtained HNSC gene expression data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and the GEO databases, andidentified aberrantly expressed RBPs between tumors and normal tissues.Meanwhile, we performed a series of bioinformatics to explore the function and prognostic value of these RBPs.Results: A total of 249 abnormally expressed RBPs were identified, including 101 down-regulated RBPs and 148 up-regulated RBPs.Using least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analysis, the fifteen RPBs were identified as hub genes. With the fifteen RPBS, the prognostic prediction model was constructed.Further analysis showed that the high-risk score of the patients expressed a better survival outcome. The prediction model was validated in another HNSC dataset, and similar findings were observed. Conclusions: Our results provide novel insights into the pathogenesis of HNSC. The fifteen RBP gene signature exhibited the predictive value of moderate HNSC prognosis, and have potential application value in clinical decision-making and individualized treatment.

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

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.

scholarly journals Integrated Analysis of the Prognosis-Associated RNA-Binding Protein Genes and Candidate Drugs in Renal Papillary Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Silin Jiang ◽  
Xiaohan Ren ◽  
Shouyong Liu ◽  
Zhongwen Lu ◽  
Aiming Xu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Biological Effects ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Poor Overall Survival ◽  
Cox Regression Analysis

RNA-binding proteins (RBPs) play significant roles in various cancer types. However, the functions of RBPs have not been clarified in renal papillary cell carcinoma (pRCC). In this study, we identified 31 downregulated and 89 upregulated differentially expressed RBPs on the basis of the cancer genome atlas (TCGA) database and performed functional enrichment analyses. Subsequently, through univariate Cox, random survival forest, and multivariate Cox regression analysis, six RBPs of SNRPN, RRS1, INTS8, RBPMS2, IGF2BP3, and PIH1D2 were screened out, and the prognostic model was then established. Further analyses revealed that the high-risk group had poor overall survival. The area under the curve values were 0.87 and 0.75 at 3 years and 0.78 and 0.69 at 5 years in the training set and test set, respectively. We then plotted a nomogram on the basis of the six RBPs and tumor stage with the substantiation in the TCGA cohort. Moreover, we selected two intersectant RBPs and evaluate their biological effects by GSEA and predicted three drugs, including STOCK1N-28457, pyrimethamine, and trapidil by using the Connectivity Map. Our research provided a novel insight into pRCC and improved the determination of prognosis and individualized therapeutic strategies.

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

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.

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