Integrated Analysis of Nine Prognostic RNA-Binding Proteins in Soft Tissue Sarcoma.
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ABSTRACT:
SUBMITTER: Lin LL
PROVIDER: S-EPMC8138553 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNeuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB.MethodsWe obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset.ResultsIn total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368-1.722, P = 2.69E-13).ConclusionsThis study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.
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Project description:BACKGROUND:Several studies have investigated the molecular drivers and therapeutic targets in adult soft tissue sarcomas. However, such studies are limited by the genomic heterogeneity and rarity of sarcomas, particularly in those with complex and unbalanced karyotypes. Additional biomarkers are needed across sarcoma types to improve therapeutic strategies. To investigate the molecular characteristics of complex karyotype sarcomas (CKSs) for therapeutic targets, we performed genomic profiling. RESULTS:The mutational landscape showed that TP53, ATRX, and PTEN genes were highly mutated. CKS samples were categorized into three groups based on copy number variations that were associated with CDK4 and RB1 signatures. Integrated analysis of genomic and transcriptomic data revealed several pathways related to PDGFR, which could be a strategic target for anti-sarcoma therapy. CONCLUSIONS:This study provides a detailed molecular classification of CKSs and proposes several therapeutic targets. Targeted or combinational therapies for treating CKS should be considered before chemotherapy.
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Project description:As the most common transcriptional regulators, zinc finer proteins (ZNFs) play vital roles in occurrence and progression of malignant tumors. Whereas, information regarding the roles of ZNFs in soft tissue sarcomas (STS) remains scarce. In this study, a comprehensive bioinformatics analysis investigating roles of ZNFs in STS was performed. Initially, we extracted raw datasets of differentially expressed ZNFs from GSE2719. Using a sequence of bioinformatics methods, we then investigated the prognostic significance, function, and molecular subtype of these differentially expressed ZNFs. In addition, CCK8 and plate clone formation assays were used to explore the effect of ZNF141 on STS cells. A total of 110 differentially expressed ZNFs were identified. Nine ZNFs (HLTF, ZNF292, ZNF141, LDB3, PHF14, ZNF322, PDLIM1, NR3C2, and LIMS2) were selected to establish an overall survival (OS) prediction model, and seven ZNFs (ZIC1, ZNF141, ZHX2, ZNF281, ZNHIT2, NR3C2, and LIMS2) were used to develop a progression-free survival (PFS) prediction model. Compared with patients with low-risk in the TCGA training and testing cohorts, as well as the GEO validation cohorts, patients with high-risk had poorer OS and PFS. Using nomograms constructed with the identified ZNFs predicting OS and PFS, we established a clinically useful model. Four distinct molecular subtypes with different prognostic and immune infiltration characteristics were identified. In vitro experiments showed that ZNF141 promoted the proliferation and viability of STS cells. In conclusion, ZNF-related models are useful as prognostic biomarkers, suggesting their potentials as therapeutic targets in STS. These findings will enable us to develop novel strategies treating STS, which will potentially improve outcomes of patients with STS.
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Project description:Lung cancer is the top cause of carcinoma-associated deaths worldwide. RNA binding proteins (RBPs) dysregulation has been reported in various malignant tumors, and that dysregulation is closely associated with tumorigenesis and tumor progression. However, little is known about the roles of RBPs in lung adenocarcinoma (LUAD). In this study, we downloaded the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. We then performed an integrative analysis to explore the expression and prognostic significance of these RBPs. A total of 164 differently expressed RBPs were identified, including 40 down-regulated and 124 up-regulated RBPs. Pathway and Gene ontology (GO) analysis indicated that the differently expressed RBPs were mainly related to RNA processing, RNA metabolic process, RNA degradation, RNA transport, splicing, localization, regulation of translation, RNA binding, TGF-beta signaling pathway, mRNA surveillance pathway, and aminoacyl-tRNA biosynthesis. Survival analysis revealed that the high expression of BOP1 or GNL3 or WDR12 or DCAF13 or IGF2BP3 or IGF2BP1 were associated with poor overall survival (OS). Conversely, overexpression of KHDRBS2/SMAD predicted high OS in these patients. ROC curve analysis showed that the eight hub genes with a better diagnostic accuracy to distinguish lung adenocarcinoma. The results provided novel insights into the pathogenesis of LUAD and the development of treatment targets and prognostic molecular markers.
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Project description:BackgroundHepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, ranks as the fifth most common cancer and has been the second most frequent cause of cancer-related death. RNA binding proteins (RBPs) are proteins that interact with different classes of RNA and are commonly detected in cells.MethodsWe used RNA sequencing data from TCGA to display dysfunctional RBPs microenvironments and provide potential useful biomarkers for HCC diagnosis and prognosis.Results330 differently expressed RBPs (208 upregulated and 122 downregulated) were identified. KEGG were mainly enriched in RNA degradation, Influenza A, Hepatitis C, RIG-I-like receptor signaling pathway, Herpes simplex virus 1 infection and RNA transport. CBioPortal results demonstrated that these genes were altered in 50 samples out of 357 HCC patients (14%) and the amplification of BRCA1 was the largest frequent copy-number alteration.ConclusionBased on the online database, we identified novel RBPs markers for the prognosis of hepatocellular carcinoma.
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Project description:RNA-binding proteins (RBPs) play important roles in many cancer types. However, RBPs have not been thoroughly and systematically studied in gliomas. Global analysis of the functional impact of RBPs will provide a better understanding of gliomagenesis and new insights into glioma therapy. In this study, we integrated a list of the human RBPs from six sources-Gerstberger, SONAR, Gene Ontology project, Poly(A) binding protein, CARIC, and XRNAX-which covered 4127 proteins with RNA-binding activity. The RNA sequencing data were downloaded from The Cancer Genome Atlas (TCGA) (n = 699) and Chinese Glioma Genome Atlas (CGGA) (n = 325 + 693). We examined the differentially expressed genes (DEGs) using the R package DESeq2, and constructed a weighted gene co-expression network analysis (WGCNA) of RBPs. Furthermore, survival analysis was also performed based on the univariate and multivariate Cox proportional hazards regression models. In the WGCNA analysis, we identified a key module involved in the overall survival (OS) of glioblastomas. Survival analysis revealed eight RBPs (PTRF, FNDC3B, SLC25A43, ZC3H12A, LRRFIP1, HSP90B1, HSPA5, and BNC2) are significantly associated with the survival of glioblastoma patients. Another 693 patients within the CGGA database were used to validate the findings. Additionally, 3564 RBPs were classified into canonical and non-canonical RBPs depending on the domains that they contain, and non-canonical RBPs account for the majority (72.95%). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that some non-canonical RBPs may have functions in glioma. Finally, we found that the knockdown of non-canonical RBPs, PTRF, or FNDC3B can alone significantly inhibit the proliferation of LN229 and U251 cells. Simultaneously, RNA Immunoprecipitation (RIP) analysis indicated that PTRF may regulate cell growth and death- related pathways to maintain tumor cell growth. In conclusion, our findings presented an integrated view to assess the potential death risks of glioblastoma at a molecular level, based on the expression of RBPs. More importantly, we identified non-canonical RNA-binding proteins PTRF and FNDC3B, showing them to be potential prognostic biomarkers for glioblastoma.
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Project description:BackgroundThe PD-1/PD-L1 axis plays a paramount role in the immune escape of tumor cells by negative regulation of T-cell functions. The aim of the present study was to characterize the PD-L1 expression pattern and its clinical implication in soft-tissue sarcomas (STS).MethodsWe analyzed PD-L1 expression in 82 STS patients with 5 subtypes: rhabdomyosarcoma, synovial sarcoma, Ewing sarcoma, epithelioid sarcoma, and mesenchymal chondrosarcoma.ResultsThe median age at diagnosis was 26 (range: 1-78) and the male to female ratio was 1.6. The majority (80 %) of patients showed locoregional disease rather than metastatic disease at diagnosis. Thirty-five cases (43 %) showed PD-L1 expression and the proportion of PD-L1 expression was significantly different according to histologic subtypes (P = 0.004); highest in epithelioid sarcoma (100 %, 7/7), followed by synovial sarcoma (53 %, 10/19), rhabdomyosarcoma (38 %, 12/32), and Ewing sarcoma (33 %, 6/18), while it was not expressed in mesenchymal chondrosarcoma (0 %, 0/6). STS patients with PD-L1 expression had worse overall survival compared with those without PD-L1 expression (5-year survival rate: 48 % vs. 68 %, P = 0.015). The Cox proportional hazard model adjusted for histologic subtype, initial metastasis, and PD-L1 expression showed that PD-L1 expression was significantly associated with shorter overall survival (P = 0.037, HR 2.57, 95 % CI 1.060-6.231).ConclusionWe have confirmed PD-L1 expression in various STS of young population and demonstrated its independent negative prognostic role, thereby suggesting the PD-1/PD-L1 axis as a potential therapeutic target for the treatment of young STS patients.
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Project description:BackgroundSoft-tissue sarcomas (STS) constitute a rare group of heterogeneous mesenchymal tumours containing more than 100 histologic subtypes. Here, we investigate whether, and if so, to what extent, skeletal metastases affect the outcome of patients with advanced or metastatic disease.Materials and methodsSelected patients participated in five clinical trials of EORTC-STBSG. Individuals were included if they started treatment with an active drug and had advanced/metastatic STS. The endpoints of interest were overall survival (OS) and progression-free survival (PFS). Univariate and multivariate pooled analyses (after correcting for 12 covariates) were employed with Kaplan-Meier and Cox regression to model the impact of bone metastasis at presentation per treatment line stratified by study. For the subset of patients with bone metastasis, the impact of another metastatic organ site was explored with multivariate Cox regression models.Results565 out of 1034 (54.6%) patients received first-line systemic treatment for locally advanced or metastatic disease. Bone metastases were present in 140 patients (77 first-line, 63 second-line or higher). The unadjusted difference in OS/PFS with or without bone metastasis was statistically significant only for first-line patients. For OS, the adjusted hazard ratios for bone metastasis presence were 1.33 (95%-CI: 0.99-1.78) and 1.11 (95%-CI: 0.81-1.52) for first-line/second-line or higher treated patients, respectively. Likewise, the adjusted hazard ratios for PFS were 1.31 (95%-CI: 1.00-1.73) and 1.07 (95%-CI: 0.80-1.43). Effects were not statistically significant, despite a trend in first-line patients for both endpoints. Subgroup analyses indicated bone and lymph node metastasis as the most detrimental combination for OS and bone and lung metastasis for PFS.ConclusionsAdult STS patients receiving palliative systemic therapy with bone metastasis carried an overall worse prognosis than STS patients without bone metastases. Skeletal metastasis was detrimental for both OS and PFS, independent of the treatment line. Findings may have implications for the management of these patients.
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Project description:Background and Purpose: Breast cancer is one of the leading causes of death among women. RNA binding proteins (RBPs) play a vital role in the progression of many cancers. Functional investigation of RBPs may contribute to elucidating the mechanisms underlying tumor initiation, progression, and invasion, therefore providing novel insights into future diagnosis, treatment, and prognosis. Methods: We downloaded RNA sequencing data from the cancer genome atlas (TCGA) by UCSC Xena and identified relevant RBPs through an integrated bioinformatics analysis. We then analyzed biological processes of differentially expressed genes (DEGs) by DAVID, and established their interaction networks and performed pathway analysis through the STRING database to uncover potential biological effects of these RBPs. We also explored the relationship between these RBPs and the prognosis of breast cancer patients. Results: In the present study, we obtained 1092 breast tumor samples and 113 normal controls. After data analysis, we identified 90 upregulated and 115 downregulated RBPs in breast cancer. GO and KEGG pathway analysis indicated that these significantly changed genes were mainly involved in RNA processing, splicing, localization and RNA silencing, DNA transposition regulation and methylation, alkylation, mitochondrial gene expression, and transcription regulation. In addition, some RBPs were related to histone H3K27 methylation, estrogen response, inflammatory mediators, and translation regulation. Our study also identified five RBPs associated with breast cancer prognosis. Survival analysis found that overexpression of DCAF13, EZR, and MRPL13 showed worse survival, but overexpression of APOBEC3C and EIF4E3 showed better survival. Conclusion: In conclusion, we identified key RBPs of breast cancer through comprehensive bioinformatics analysis. These RBPs were involved in a variety of biological and molecular pathways in breast cancer. Furthermore, we identified five RBPs as a potential prognostic biomarker of breast cancer. Our study provided novel insights to understand breast cancer at a molecular level.
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Project description:Lung cancer is the leading cause of cancer-related deaths worldwide. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma (LUSC) remain unclear. In this study, we obtained gene expression data and corresponding clinical information for LUSC from The Cancer Genome Atlas (TCGA) database, identified aberrantly expressed RBPs between tumors and normal tissue, and conducted a series of bioinformatics analyses to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained, comprising 59 downregulated and 241 upregulated RBPs. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with mRNA metabolic processes, RNA processing, RNA modification, regulation of translation, the TGF-beta signaling pathway, and the Toll-like receptor signaling pathway. Nine RBP genes (A1CF, EIF2B5, LSM1, LSM7, MBNL2, RSRC1, TRMU, TTF2, and ZCCHC5) were identified as prognosis-associated hub genes by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier survival, and multivariate Cox regression analyses, and were used to construct the prognostic model. Further analysis demonstrated that high risk scores for patients were significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.712 at 3 years and 0.696 at 5 years. We also developed a nomogram based on nine RBP genes, with internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis in LUSC. Our results provide novel insights into the pathogenesis of LUSC. The nine-RBP gene signature showed predictive value for LUSC prognosis, with potential applications in clinical decision-making and individualized treatment.
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