scholarly journals Integrating 13 Microarrays to Construct a 6 RNA-binding proteins Prognostic Signature for Gastric Cancer patients

2021 ◽  
Vol 12 (16) ◽  
pp. 4971-4984
Author(s):  
Liqiang Zhou ◽  
Qi Zhou ◽  
You Wu ◽  
Lin Xin
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Prognostic Signature ◽  
Gastric Cancer Patients

scholarly journals Identification and validation of an immune-associated RNA-binding proteins signature to predict clinical outcomes and therapeutic responses in colon cancer patients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Di Sun ◽  
Kui-Sheng Yang ◽  
Jian-Liang Chen ◽  
Zheng-bing Wang
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Binding Proteins ◽  
Drug Sensitivity ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Immune Infiltration ◽  
Tcga Dataset

Abstract Background The immune infiltration of patients with colon cancer (CC) is closely associated with RNA-binding proteins (RBPs). However, immune-associated RBPs (IARBPs) in CC remain unexplored. Methods The data were downloaded from The Cancer Genome Atlas (TCGA) and the patients were divided into four immune subgroups by single sample gene set enrichment analysis (ssGSEA), in which weighted gene correlation network analysis (WGCNA) identified modules of co-expressed genes correlated with immune infiltration. Univariate (UCR) and multivariate Cox regression (MCR) analyses were applied to screen survival-associated IARBPs. Then, a prognostic signature was performed on TCGA dataset. Risk model was constructed based on the TCGA dataset. Based on the median risk score, CC patients were subdivided into low- and high-risk groups. Furthermore, the accuracy and prognostic value of this signature were validated by using Kaplan-Meier (K-M) curve, receiver operating characteristic (ROC). We further validated the findings in Gene Expression Omnibus (GEO) database. Finally, we evaluated the association between gene expression level and drug sensitivity. Results Based on the infiltration of immune cells, the TCGA patients were divided into four subgroups. In total, we identified 25 IARBPs, after differential expression and WGCNA analysis. Subsequently, two IARBP signatures (FBXO17 and PPARGC1A) were identified to be significantly associated with the overall survival (OS) of CC patients. K-M survival analysis revealed that the low-risk group correlated with prolonged OS. The prognostic signature was an independent prognostic factor and reflects the immune status of CC patients. Finally, FBXO17 was related with drug sensitivity of bleomycin, gemcitabine, and lenvatinib. PPARGC1A was related to drug sensitivity of dabrafenib, vemurafenib, and trametinib. Conclusion A novel two immune-associated RBPs that was established that may be useful in predicting survival and individualized treatment.

scholarly journals Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Shan Yu ◽  
Yan Wang ◽  
Ke Peng ◽  
Minzhi Lyu ◽  
Fenglin Liu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cancer Patients ◽  
Roc Analysis ◽  
Checkpoint Inhibitors ◽  
Cox Proportional Hazards Model ◽  
Prognostic Signature ◽  
Risk Patients ◽  
Gastric Cancer Patients

Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.

scholarly journals Identification and Validation of an RNA Binding Proteins-Associated Prognostic Signature for Non-Small Cell Lung Cancer

2020 ◽  
Author(s):  
Ti-wei Miao ◽  
Fang-ying Chen ◽  
Wei Xiao ◽  
Long-yi Du ◽  
Bing Mao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Small Cell ◽  
Functional Enrichment ◽  
Prognostic Signature ◽  
Small Cell Lung

Abstract Background: Non-small cell lung cancer (NSCLC) is a malignancy with relatively high incidence and poor prognosis. RNA-binding proteins (RBPs) were reported to be dysregulated in multiple cancers and were closely associated with tumor initiation and progression. However, the functions of RBPs in NSCLC remain unclear. Method: The RNA sequencing data and corresponding clinical information of NSCLC was downloaded from The Cancer Genome Atlas (TCGA) database. We identified aberrantly expressed RBPs between tumor and control tissue, and systemically investigated the expression and prognostic value of these RBPs by a series of bioinformatics analysis.Results: A total of 459 aberrantly expressed RBPs (291 up-regulated and 168 down-regulated RBPs) were identified. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with RNA splicing, ncRNA metabolic process, regulation of translation, mRNA surveillance pathway, RNA degradation, and RNA transport. Thirteen RBPs (ZC3H12C, ZC3H12D, BOP1, CASC3, DDX24, IGF2BP1, KHDC1, FASTKD3, TARBP1, INTS7, NOL12, SNRPB, PABPC1L) were identified as prognostic RBPs by multivariate Cox regression analysis, and were used to construct a prognostic signature. Further analysis demonstrated that high-risk group were significantly related to poor overall survival in training and testing cohort. The area under receiver operator characteristic curve of the prognostic signature was 0.703 in training cohort and 0.636 in testing cohort. In addition, the prognostic signature was further validated in differently clinical subgroup (>=65, <65, female, male, stage I-II, III- IV, T1-2, T3-4, N0, N1-3, M0 and M1). The risk score was an independent prognostic factor of NSCLC. A nomogram based on thirteen RBPs was constructed to predict the survival of patients.Conclusion: Our results provide novel insights into the pathogenesis of NSCLC. The RBPs-associated prognostic signature showed predictive value for NSCLC prognosis, with potential applications in clinical decision-making and individualized treatment.

scholarly journals Development and validation of a six-RNA binding proteins prognostic signature and candidate drugs for prostate cancer

Genomics ◽  
2020 ◽  
Vol 112 (6) ◽  
pp. 4980-4992
Author(s):  
Lei Gao ◽  
Jialin Meng ◽  
Yong Zhang ◽  
Junfei Gu ◽  
Zhenwei Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Prognostic Signature ◽  
Development And Validation

scholarly journals Development and validation of a six-RNA binding proteins prognostic signature and candidate drugs for prostate cancer

2020 ◽  
Author(s):  
Lei Gao ◽  
Jialin Meng ◽  
Yong Zhang ◽  
Junfei Gu ◽  
Zhenwei Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Risk Group ◽  
Life Quality ◽  
High Risk Group ◽  
Prognostic Signature

AbstractThe dysregulation of RNA binding proteins (RBPs) play critical roles in the progression of several cancers. However, the overall functions of RBPs in prostate cancer (PCa) remain poorly understood. Therefore, we first identified 144 differentially expressed RBPs in tumors compared to normal tissues based on the TCGA dataset. Next, six RBP genes (MSI1, MBNL2, LENG9, REXO2, RNASE1, PABPC1L) were screened out as prognosis hub genes by univariate, LASSO and multivariate Cox regression and used to establish the prognostic signature. Further analysis indicated that high risk group was significantly associated with poor RFS, which was validated in the MSKCC cohort. Besides, patients in high risk group was closely associated with dysregulation of DNA damage repair pathway, copy number alteration, tumor burden mutation and low-respond to cisplatin (P < 0.001), bicalutamide (P < 0.001). Finally, three drugs (ribavirin, carmustine, carbenoxolone) were predicted using Connectivity Map. In summary, we identified a six-RBP gene signature and three candidate drugs against PCa, which may promote the individualized treatment and further improve the life quality of PCa patients.

scholarly journals Systematic Construction and Validation of an RNA Binding Proteins-Based Signature for Prognostic Prediction in Gastric Cancer

2020 ◽  
Author(s):  
Yi Zhang ◽  
Yuzhi Wang ◽  
Chengwen Li ◽  
Tianhua Jiang
Keyword(s):  
Gastric Cancer ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Risk Group ◽  
Low Risk ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Incidence And Mortality

Abstract Background: Gastric cancer (GC) is one of the most common cancers with high incidence and mortality worldwide. Recently, RNA-binding proteins (RBPs) have drawn more and more attention for its role in cancer pathophysiology. In this study, we aim to explore the function and clinical implication of RBPs in GC. Methods: RNA sequencing data along with the corresponding clinical information of GC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA-binding proteins (DERBPs) between tumor and normal tissues were identified by ‘limma’ package. Functional enrichment analysis and the protein-protein interaction (PPI) network were harnessed to explore the function and interaction of DERBPs. Next, Univariate and multiple Cox regression were applied to screen prognosis-related hub RBPs and to construct a signature for BC. Meanwhile, a nomogram was built based on the same RBPs. Results: A total of 296 DERBPs were found, and most of them mainly related to post-transcriptional regulation of RNA and ribonucleoprotein. A PPI network of DERBPs was constructed, consisting of 262 nodes and 2567 edges. A prognostic signature was built depended on seven prognosis-related hub RBPs that could divide GC patients into high- and low-risk groups. Survival analysis showed that the high-risk group had a worse prognosis compared to the low-risk group and the time-dependent receiver operating characteristic (ROC) curves suggested that the signature existed moderate predictive capacities of survival for GC patients. Similar results were obtained from another independent set GSE84437, confirming the robustness of signature. Calibration plots reported good consistency between overall survival (OS) prediction by nomogram and actual observation. Conclusion: The findings of this study would provide evidence of the effect of RBPs on GC as well as offering novel potential biomarkers in prognosis prediction and clinical decision for GC patients.

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