Construction And Study Of Radiosensitivity Prediction Model For Low-grade Gliomas Based On Transcriptome Data

PURPOSE: Low-grade gliomas are typically slow-growing central nervous system tumors that can transform into more aggressive types within a decade.Radiotherapy is an effective treatment to inhibit the progression of malignant tumors,including low-grade gliomas.However,the role of radiotherapy in low-grade gliomas is not well understood,and patients with low-grade gliomas have significant differences in their response and radiosensitivity to ionizing radiation due to their genetic characteristics,thus studies related to radiosensitivity have received much attention as a focus.The study of radiosensitivity biomarkers has established a reliable theoretical basis for the clinical treatment and diagnosis of low-grade gliomas as well as for prognostic assessment.In this study,we used bioinformatics to identify gene signatures associated with radiosensitivity in low-grade glioma,and constructed a prognostic risk model for patients with low-grade glioma and conducted a comprehensive study.Methods: The gene expression data from 514 patients was downloaded from The Cancer Genome Atlas(TCGA).LASSO Cox regression was used to identify six radiosensitivity-related genes in the TCGA-LGG cohorts.A prognostic model was constructed based on the coefficient value of selected genes in multivariate Cox proportional hazard regression.The probability of individual survival was then predicted using a nomogram.Differences in tumor immune and microenvironment between high-and low-risk groups were analyzed.Results: We constructed a prognostic radiosensitivity-related gene signature(Rad RGSig)for patients with LGGs.Kaplan–Meier survival curve analysis revealed a significantly better prognosis for low-risk group than for high-risk group(P < 0.001)and the ROC curves showed the accuracy of1-,3-,and 5-years were(0.869,0.912,and 0.873).The Rad RGSig was determined to be an independent prognostic factor [hazard ratio(HRs)= 1.159,95% confidence interval(CI)=1.102–1.219,P < 0.001].Moreover,the immune-related analysis showed Rad RGSig revealed significant differences in radiosensitivity between high-and low-risk group.Conclusions: We identified TMSB4 X,IGFBP5,MSN,RPN2,CDKN2 C,and PTGFRN as the gene signature for predicting the prognosis for patients receiving radiotherapy in LGGs.Figure11 Table2 Reference172...