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A Novel RNA-Binding Protein Signature to Predict Clinical Outcomes and Guide Clinical Therapy in Gastric Cancer.

ABSTRACT: Objective: This study aimed to develop an RNA-binding protein (RBP)-based signature for risk stratification and guiding clinical therapy in gastric cancer. Methods: Based on survival-related RBPs, an RBP-based signature was established by LASSO regression analysis in TCGA dataset. Kaplan-Meier curves were drawn between high- and low-risk groups. The predictive efficacy of this signature was assessed via ROCs at 1-, 3-, and 5-year survival. Its generalizability was verified in an external dataset. Following adjustment with other clinicopathological characteristics, the independency of survival prediction was evaluated via multivariate Cox regression and subgroup analyses. GSEA was utilized in identifying activated pathways in two groups. Stromal score, immune score, tumor purity, and infiltration levels of 22 immune cells were determined in each sample via the ESTIMATE and CIBERSORT algorithms. The sensitivity to chemotherapy drugs was assessed through the GDSC database. Results: Data showed that patients with high risk exhibited unfavorable clinical outcomes than those with low risk. This signature possessed good performance in predicting 1-, 3-, and 5-year survival and can be independently predictive of patients' survival. Calcium, ECM receptor interaction, and focal adhesion were highly enriched in high-risk samples. High-risk samples presented increased stromal and immune scores and reduced tumor purity. Moreover, this signature presented close relationships with immune infiltrations. Low-risk specimens were more sensitive to sorafenib, gefitinib, vinorelbine, and gemcitabine than high-risk specimens. Conclusion: This RBP-based signature may be a promising tool for predicting clinical outcomes and guiding clinical therapy in gastric cancer.

SUBMITTER: Qiu Z

PROVIDER: S-EPMC8319385 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The aim of this study was to build a prognostic model for endometrial cancer (EC) patients based on RNA-binding proteins (RBPs). RNA sequencing and clinical data for uterine corpus EC (UCEC) were obtained from The Cancer Genome Atlas (TCGA). Univariate and multivariate Cox regression analysis were used to obtain the following risk formula: score = sum (corresponding coefficient × expression of each gene). A nomogram was developed to accurately predict patient survival based on risk score, age, stage, and grade. Immunohistochemistry (IHC) was used to verify the expression of RBPs in EC. The mRNA expression of RBPs was measured by qRT-PCR. The effects of RBPs on the malignant biologic behavior of EC were detected using CCK-8, colony formation, and transwell invasion assays. A novel prognostic signature was constructed, comprising three RBPs (CD3EAP, SBDS, and TDRKH). The risk score was: Risk score = (0.860 × CD3EAP expression) + (0.622 × 6SBDS expression) + (0.427 × 4TDRKH expression). The area under the receiver operating characteristic curves (AUCs) of risk score for 1-, 3-, and 5-year overall survival (OS) was 0.75, 0.68, and 0.65, respectively. The AUCs of the nomogram for 1-, 3-, and 5-year OS were 0.811, 0.793, and 0.814, respectively. In our independent cohort, the IHC results revealed that CD3EAP (P < 0.001) and TDRKH (P < 0.001) were upregulated and SBDS (P < 0.001) was downregulated in EC. Immunostaining showed the expression levels of CD3EAP, SBDS and TDRKH for each patient and these were multiplied by their respective coefficients of 0.860, 0.622 and 0.427 to obtain the risk scores. The AUCs of risk score for 1-, 3-, and 5-year OS were 0.888, 0.793, and 0.780 respectively. The AUCs of the nomogram for 1-, 3-, and 5-year OS were 0.790, 0.826, and 0.906, respectively. Cell functional experiments also confirmed the influence of the key RBPs on the malignant biologic behavior of EC. In summary, a characteristic model based on our three RBPs accurately predicted the prognosis of EC. Our in-depth analysis of these RBPs may inform the development of novel strategies for the diagnosis and treatment of EC.

Project description:BackgroundThe 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.MethodsThe 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.ResultsBased 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.ConclusionA novel two immune-associated RBPs that was established that may be useful in predicting survival and individualized treatment.

Project description:Objective: Glioma is the most common and fatal primary brain tumor that has a high risk of recurrence in adults. Identification of predictive biomarkers is necessary to optimize therapeutic strategies. This study investigated the predictive efficacy of a previously identified radiosensitivity signature as well as Exportin 1 (XPO1) expression levels. Methods: A total of 1,552 patients diagnosed with glioma were analyzed using the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases. The radiosensitive and radioresistant groups were identified based on a radiosensitivity signature. Patients were also stratified into XPO1-high and XPO1-low groups based on XPO1 mRNA expression levels. Overall survival rates were compared across patient groups. Differential gene expression was detected and analyzed through pathway enrichment and Gene Set Enrichment Analysis (GSEA). To predict 1-, 3-, and 5-years survival rates for glioma patients, a nomogram was established combining the radiosensitivity gene signature, XPO1 status, and clinical characteristics. An artificial intelligence clustering system and a survival prediction system of glioma were developed to predict individual risk. Results: This proposed classification based on a radiosensitivity gene signature and XPO1 expression levels provides an independent prognostic factor for glioma. The RR-XPO1-high group shows a poor prognosis and may benefit most from radiotherapy-combined anti-XPO1 treatment. The nomogram based on the radiosensitivity gene signature, XPO1 expression, and clinical characteristics performs more optimally compared to the WHO classification and IDH status in predicting survival rates for glioma patients. The online clustering and prediction systems make it accessible to predict risk and optimize treatment for a special patient. The cell cycle, p53, and focal adhesion pathways are associated with more invasive glioma cases. Conclusion: Combining the radiosensitivity signature and XPO1 expression is a favorable approach to predict outcomes as well as determine optimal therapeutic strategies for glioma patients.

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A Novel RNA-Binding Protein Signature to Predict Clinical Outcomes and Guide Clinical Therapy in Gastric Cancer.

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