Identification Of Monosaccharide Biomarkers As A Prognostic Model Of Bladder Cancer

Objective: Carbohydrates play an important role in the occurrence and development of tumors.The monosaccharide prognostic model was constructed by the difference between the serum monosaccharide content of bladder cancer patients and healthy people,and the feasibility of the model as a prognostic biomarker of bladder cancer was evaluated.Methods: The experimental group selected 200 patients who were diagnosed with bladder cancer in the Affiliated Hospital of Qingdao University from February 1,2018 to September30,2021,and 200 people who matched the age and gender of the patients in the experimental group for physical examination during the same period.The differences in monosaccharide levels between the two groups were analyzed by PCR instrument-assisted acid hydrolysis,and Lasso-penalized Cox regression analysis was used to construct a monosaccharide marker model to divide bladder cancer patients into a low-risk group and a high-risk group.The survival analysis was used to determine the model to predict the overall survival of patients with bladder cancer.Univariate and multivariate Cox analysis was used to determine the influence of the model and other clinical factors on the prognosis of patients.The ROC curve analysis and DCA clinical decision curve analysis were used to evaluate the prediction performance of the model.Nomogram to predict future survival of patients.The significant difference in overall survival between the high and low risk groups revealed by the survival analysis was verified by the log-rank test.All data analysis was performed using SPSS26.0,R(v.4.1.2,The Comprehensive R Archive Network,open source)software,and the graphing was performed by R software.P values for all data are two-tailed,and P values< 0.05 were considered statistically significant.Results: By comparing the levels of monosaccharides between 200 bladder cancer patients and 200 healthy subjects,it was found that the monosaccharide levels in the bladder cancer group were significantly higher than those in the healthy group,and the difference was statistically significant(P<0.05).Based on this,we used Lasso-penalized Cox regression analysis to construct a monosaccharide-related diagnostic and prognostic model,and calculated the risk score according to the expression level of each monosaccharide and its corresponding regression coefficient =(0.7×Exp Glc N)+(0.568×Exp Gal)+(0.09×Exp Gal N)+(0.508×Exp Fuc),and according to the median risk score,patients were divided into high-risk group and low-risk group.According to Kaplan-Meier survival analysis,the survival rate of the high-risk group was lower than that of the low-risk group(P<0.001).Univariate Cox regression analysis showed that the model([HR]=1.769,[CI]: 1.387-2.257,P < 0.001)was associated with overall survival in bladder cancer patients.Multivariate Cox regression analysis showed that the model([HR]=1.516,[CI]: 1.172-1.961,P < 0.01)was an independent risk factor affecting the prognosis of bladder cancer patients.This suggests that the model can be involved as an independent factor in the prediction of survival in bladder cancer patients.After ROC curve evaluation,the study found that the model had an AUC value of 0.743 within three years,which was superior to traditional clinical features for bladder cancer prediction analysis.DCA decision analysis shows that the model has the largest curve slope compared with other factors,indicating that under the condition of obtaining the same net benefit,the larger the threshold range,the safer it is,which further illustrates the model’s risk diagnosis compared with other traditional clinical factors.the superiority.To facilitate clinical application,we constructed a new nomogram including age,sex,grade,TNM stage and monosaccharide risk model to predict the 1,3,and 5-year survival of bladder cancer patients.The score of each factor in the nomogram predicts the patient’s future.Limited by the study time,the sample size of this study is insufficient,the excessive pursuit of uniform control settings cannot avoid the occurrence of selection bias,and the lack of long-term follow-up results cannot predict long-term survival results;secondly,the model validation group was not set up;this study Each monosaccharide component in tumor tissue has not been verified,and the related mechanism research has not been studied.Conclusions: Through Lasso regression analysis,we constructed a risk assessment model based on glucosamine,galactose,galactosamine,fucose levels and regression correlation coefficients.After survival analysis,univariate Cox analysis,multivariate Cox analysis,ROC curve analysis,and DCA curve analysis,it was confirmed that the monosaccharide model has a good predictive ability for the diagnosis and prognosis of bladder cancer patients,and the future survival of patients can be predicted by nomogram.This study is the first to construct and verify the predictive ability of a model composed of multiple monosaccharides in bladder cancer patients,revealing the research value of monosaccharides in bladder cancer risk prognosis.