Table_1_A Novel RNA-Binding Protein Signature to Predict Clinical Outcomes and Guide Clinical Therapy in Gastric Cancer.XLSX (32.25 kB)
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Table_1_A Novel RNA-Binding Protein Signature to Predict Clinical Outcomes and Guide Clinical Therapy in Gastric Cancer.XLSX

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posted on 15.07.2021, 04:12 authored by Zhigang Qiu, Haitao Jiang, Kun Ju, Xichun Liu

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.

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