A Prognostic Model Constructed Based on Rna Binding Protein to Predict the Prognosis of Patients With Rectal Cancer

Abstract Background: The incidence of rectal cancer in young people is increasing, and there has been a problem of poor prognosis in recent years. Many studies have shown that RNA binding protein (RBP) is related to the progression of various malignant tumors. However, the role of RBPs in rectal cancer is poorly understood. New prognostic models are urgently needed.Materials and methods: In the study, we used the RBPTD database, The Cancer Genome Atlas (TCGA) database and the transcription data information and corresponding clinical information of rectal cancer patients in the Gene Expression Omnibus (GEO) database to screen out RBPs that are differentially expressed in tumor tissues and normal tissues. Subsequently, we analyzed the prognostic value of these RBPs using bioinformatics methods. In order to screen the key RBP in the occurrence of rectal tumors and establish a prognostic risk score model. The use of survival analysis shows that assessing the relationship between key RBPs and the patient's overall survival rate. In the TCGA cohort, the prognostic model was further tested. At the same time, the nomogram of the 6 RBP mRNAs in the TCGA cohort was constructed, and the ROC curve was used for verification. Finally, q-PCR was performed on clinical samples to verify the expression of hub genes.Results: The new 6RBP (EXO1, TOP2A, RUVBL1, NXT1, PACSIN2, WDR4) prognostic model was established to predict the prognosis of rectal cancer. The ROC curve showed good results in the training cohort and validation cohort. The new 6RBP (EXO1, TOP2A, RUVBL1, NXT1, PACSIN2, WDR4) prognostic model was established to predict the prognosis of rectal cancer. The ROC curve showed good survival prediction in both the training cohort and the validation cohort. The constructed nomogram has certain guiding significance for clinical decision-making. In addition, GSEA analysis revealed potential biological functions. The q-PCR verification results showed the consistency with the construction of the prognostic model.Conclusions: We constructed a six RBPs prognostic model and a nomogram to predict the prognosis of patients with rectal cancer, and performed q-PCR expression testing through clinical samples, which may help clinical decision-making.