Identification of an RNA-Binding-Protein-Based Prognostic Model for Ewing Sarcoma.
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ABSTRACT:
SUBMITTER: Chen Y
PROVIDER: S-EPMC8345188 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Although multiple prognostic variables have been proposed for Ewing sarcoma (EWS), little work has been done to further categorize these variables into prognostic groups for risk classification.We derived initial prognostic groups from 2,124 patients with EWS in the SEER database. We constructed a multivariable recursive partitioning model of overall survival using the following covariates: age; stage; race/ethnicity; sex; axial primary; pelvic primary; and bone or soft tissue primary. Based on this model, we identified risk groups and estimated 5-year overall survival for each group using Kaplan-Meier methods. We then applied these groups to 1,680 patients enrolled on COG clinical trials.A multivariable model identified five prognostic groups with significantly different overall survival: (i) localized, age <18 years, non-pelvic primary; (ii) localized, age <18, pelvic primary or localized, age ?18, white, non-Hispanic; (iii) localized, age ?18, all races/ethnicities other than white, non-Hispanic; (iv) metastatic, age <18; and (v) metastatic, age ?18. These five groups were applied to the COG dataset and showed significantly different overall and event-free survival based upon this classification system (P?<?0.0001). A sub-analysis of COG patients treated with ifosfamide and etoposide as a component of therapy evaluated these findings in patients receiving contemporary therapy.Recursive partitioning analysis yields discrete prognostic groups in EWS that provide valuable information for patients and clinicians in determining an individual patient's risk of death. These groups may enable future clinical trials to adjust EWS treatment according to individualized risk.
| S-EPMC5011751 | biostudies-literature
Project description:BackgroundColorectal cancer (CRC) is a kind of gastrointestinal tumor with serious high morbidity and mortality. Several reports have implicated the disorder of RNA-binding proteins (RBPs) in plenty of tumors, associating it to tumorigenesis and disease progression. The study is intended to construct novel prognostic biomarkers associated with CRC patients.MethodsData of gene expression was acquired from the TCGA database, prognosis-related genes were selected. Besides, we analyzed GO and KEGG pathways. Univariate and multivariate Cox analyses were performed to generate a prognostic-related gene signature, which was evaluated by the Kaplan-Meier (K-M) and the Receiver Operating Characteristic (ROC) curve. The independent prognostic factor was established by survival analysis. GSE38832 dataset was used to validate the signature. Finally, expression of 8 genes was further confirmed by qRT-PCR in SW480 and SW620 cell lines.ResultsWe obtained 224 differentially expressed RBPS in total, of which 78 were downregulated and 146 were upregulated. Univariate COX analysis was conducted in the TCGA cohort to select 13 RBPs with P < 0.005, stepwise multivariate COX regression analysis was used to construct an 8-RBP signature (TERT, PPARGC1A, BRCA1, CELF4, TDRD7, LUZP4, PNLDC1, ZC3H12C). Based on the model, systematic analysis illustrated that a high risk score was obviously connected to a poor prognosis. The prognostic value of the risk score was validated in GSE38832 dataset, indicating that the risk model was accurate and effective. The prognostic signature-based risk score was identified as an independent prognostic indicator for CRC. The expression results of qRT-PCR were consistent with the results of differential expression analysis.ConclusionsThe eight-RBP signature can predict the survival of CRC patients and potentially act as CRC prognostic biomarker.
| S-EPMC8182183 | biostudies-literature
Project description: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.
| S-EPMC8107400 | biostudies-literature
Project description:Lymphotoxin β receptor (LTβR) signaling is crucial for lymphoid tissue organogenesis and immune homeostasis. To identify novel regulatory mechanisms for signaling, we implemented a two-step screen that uses coexpression analysis of human fibroblasts undergoing LTβR stimulation and affinity-purification mass spectrometry for the LTβR signaling protein TNFR-associated factor 3 (TRAF3). We identify Ewing sarcoma (EWS) protein as a novel LTβR signaling component that associates with TRAF3 but not with TNFR-associated factor 2 (TRAF2). The EWS:TRAF3 complex forms under unligated conditions that are disrupted following activation of the LTβR. We conclude that EWS limits expression of proinflammatory molecules, GM-CSF, and ERK-2, promoting immune homeostasis.
| S-EPMC7033016 | biostudies-literature
Project description:BackgroundEwing sarcoma (ES), the second most prevalent bone malignant tumor has no widely known prognostic biomarker. Earlier studies have suggested that chaperonin containing TCP1 complex 6A (CCT6A), which encodes a molecular protein chaperone, is involved in the pathogenesis of many cancers. However, there are no known reports providing clear evidence of its role in ES pathogenesis.MethodsWe performed a bioinformatic analysis of 32 ES specimens from the GSE17618 dataset concentrating on the differences in gene expression, OS, event-free survival (EFS) in the different subgroups. Immunohistochemical studies were also performed to identify the expression levels of selected genes in ES and immediate paracancerous tissues.ResultsAfter 3 screenings, CCT6A was identified to be highly correlated with ES prognosis. Our survival analysis revealed a low overall survival (OS) for high CCT6A expression (P-value = .024). Our Cox regression analysis identified CCT6A expression, lEFS, and age were strongly associated with prognosis of ES. Our multivariate Cox regression analysis shows that CCT6A (P-value = .015), age (P-value = .026), and EFS (P-value = .002) were independent poor prognostic biomarkers. Our immunohistochemical analysis showed that the expression levels of CCT6A were significantly higher in ES tissues compared to the paracancerous tissues.ConclusionFrom the results of our study, we identified the expression levels of CCT6A to be strongly associated with prognosis of ES. Thus, the expression levels of the CCT6A gene could serve as a biomarker for the prediction of ES prognosis.
| S-EPMC7850773 | biostudies-literature
Project description:EWSR1 (Ewing Sarcoma Related protein 1) is an RNA binding protein that is ubiquitously expressed across cell lines and involved in multiple parts of RNA processing, such as transcription, splicing, and mRNA transport. EWSR1 has also been implicated in cellular mechanisms to control formation of R-loops, a three-stranded nucleic acid structure consisting of a DNA:RNA hybrid and a displaced single-stranded DNA strand. Unscheduled R-loops result in genomic and transcription stress. Loss of function of EWSR1 functions commonly found in Ewing Sarcoma correlates with high abundance of R-loops. In this study, we investigated the mechanism for EWSR1 to recognize an R-loop structure specifically. Using electrophoretic mobility shift assays (EMSA), we detected the high affinity binding of EWSR1 to substrates representing components found in R-loops. EWSR1 specificity could be isolated to the DNA fork region, which transitions between double- and single-stranded DNA. Our data suggests that the Zinc-finger domain (ZnF) with flanking arginine and glycine rich (RGG) domains provide high affinity binding, while the RNA recognition motif (RRM) with its RGG domains offer improved specificity. This model offers a rational for EWSR1 specificity to encompass a wide range in contexts due to the DNA forks always found with R-loops.
| S-EPMC10827230 | biostudies-literature
Project description:Ribonucleic acid-binding proteins (RBPs) are reportedly involved in tumor progression and recurrence; however, the functions and mechanisms of action of RBPs in ovarian serous cystadenocarcinoma (OSC) are not known. To address these issues, gene expression profiles of OSC tissues from The Cancer Genome Atlas (TCGA) and normal tissues from the Genotype-Tissue Expression database were compared in order to identify RBPs that are differentially expressed in OSC. We also analyzed the biological functions of these RBPs and their relationship to clinical outcome. There were 190 RBPs that were differentially expressed between OSC and normal tissues, including 93 that were upregulated and 97 that were downregulated. Five of the RBPs were used to construct a prediction model that was evaluated by univariate and multivariate Cox regression analyses. TCGA data were randomly divided into training and test cohorts, and further categorized into high- and low-risk groups according to risk score in the model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group (training cohort P = 0.0007596; test cohort P = 0.002219). The area under the receiver operating characteristic curve of the training and test cohorts was 0.701 and 0.638, respectively, demonstrating that the model had good predictive power. A nomogram was established to quantitatively describe the relationship between the five prognostic RBPs and OS in OSC, which can be useful for developing individualized management strategies for patients.
| S-EPMC7593419 | biostudies-literature
Project description:Dysregulation of RNA binding proteins (RBPs) is closely associated with tumor events. However, the function of RBPs in hepatocellular carcinoma (HCC) has not been fully elucidated. The RNA sequences and relevant clinical data of HCC were retrieved from the The Cancer Genome Atlas (TCGA) database to identify distinct RBPs. Subsequently, univariate and multivariate cox regression analysis was performed to evaluate the overall survival (OS)-associated RBPs. The expression levels of prognostic RBP genes and survival information were analyzed using a series of bioinformatics tool. A total of 365 samples with 1,542 RBPs were included in this study. One hundred and eighty-seven differently RBPs were screened, including 175 up-regulated and 12 down-regulated. The independent OS-associated RBPs of NHP2, UPF3B, and SMG5 were used to develop a prognostic model. Survival analysis showed that low-risk patients had a significantly longer OS and disease-free survival (DFS) when compared to high-risk patients (HR: 2.577, 95% CI: 1.793-3.704, P < 0.001 and HR: 1.599, 95% CI: 1.185-2.159, P = 0.001, respectively). The International Cancer Genome Consortium (ICGC) database was used to externally validate the model, and the OS of low-risk patients were found to be longer than that of high-risk patients (P < 0.001). The Nomograms of OS and DFS were plotted to help in clinical decision making. These results showed that the model was effective and may help in prognostic stratification of HCC patients. The prognostic prediction model based on RBPs provides new insights for HCC diagnosis and personalized treatment.
| S-EPMC7907500 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is one of the most prevalent malignant neoplasms with high relapse and mortality rate. It is of great importance to identify novel and effective molecular markers to predict prognosis for the treatment of HCC. The Ewing sarcoma breakpoint region 1 (EWSR1) gene is well known to fuse with various partner genes and involved in promoting the development of multiple sarcomas, especially the Ewing sarcoma family of tumors. Nevertheless, seldom studies have focused on the role of EWSR1 in cancers of epithelial origin, let alone in HCC. In the current study, the transcriptional and clinical data of EWSR1 in HCC patients were obtained from TCGA and GEO databases, as well as 124 cases from the department of Pathology of Sichuan Jianyang People's Hospital. Kaplan-Meier and Cox regression analysis were used to assess patient prognosis. EWSR1 mRNA levels were significantly upregulated in HCC tissues than in normal liver tissues (P < 0.001). The TCGA database analysis showed upregulation of EWSR1 was associated with histological grade, pathologic T stage and death, in addition to that, the T staging, N staging, TNM staging, Ki67, AFP expression were extremely higher in the EWSR1 over-expression group in our cohort. Univariate and multivariate Cox hazard regression analysis results revealed that EWSR1 was an independent prognostic factor for OS in HCC, and bioinformatics analysis showed RNA splicing process represented the major function and pathway. In conclusion, our data showed EWSR1 could serve as a novel promising prognostic biomarker for HCC patients.Abbreviations: AFP, Alpha-fetoprotein; CCL14, C-C motif chemokine ligand 14; CK19, Cytokeratin 19; CI, coefficient interval; COL1A1, Collagen 1A1; DFS, Disease-free Survival; EWSR1, Ewing Sarcoma breakpoint region 1/EWS RNA binding protein 1; FLI1, Friend leukemia virus integration 1; GEO, Gene Expression Omnibus; GO, Gene Ontology; HCC, Hepatocellular carcinoma; HR, Hazard ratio; KEGG, Kyoto Encyclopedia of Genes and Genomes; mRNA, messenger Ribonucleic Acid; N, nodule; OS, Overall survival; PPI, Protein-Protein Interaction analysis; RNA, Ribonucleic Acid; SD, Standard Deviation; TCGA, The Cancer Genome Atlas; T, tumor; TNM, tumor-nodule-metastasis.
| S-EPMC8806985 | biostudies-literature
Project description:RNA binding proteins (RBPs) are increasingly appreciated as being essential for normal hematopoiesis and have a critical role in the progression of hematological malignancies. However, their functional consequences and clinical significance in diffuse large B-cell lymphoma (DLBCL) remain unknown. Here, we conducted a systematic analysis to identify RBP-related genes affecting DLBCL prognosis based on the Gene Expression Omnibus database. By univariate and multivariate Cox proportional hazards regression (CPHR) methods, six RBPs-related genes (CMSS1, MAEL, THOC5, PSIP1, SNIP1, and ZCCHC7) were identified closely related to the overall survival (OS) of DLBCL patients. The RBPs signature could efficiently distinguished low-risk from high-risk patients and could serve as an independent and reliable factor for predicting OS. Moreover, Gene Set Enrichment Analysis revealed 17 significantly enriched pathways between high- versus low-risk group, including the regulation of autophagy, chronic myeloid leukemia, NOTCH signaling pathway, and B cell receptor signaling pathway. Then we developed an RBP-based nomogram combining other clinical risk factors. The receiver operating characteristic curve analysis demonstrated high prognostic predictive efficiency of this model with the area under the curve values were 0.820 and 0.780, respectively, in the primary set and entire set. In summary, our RBP-based model could be a novel prognostic predictor and had the potential for developing treatment targets for DLBCL.
| S-EPMC8026940 | biostudies-literature