Abstract:
Objective: To construct a prognostic model using arti cial neural network (ANN) approach, providing an idea for the prediction and diagnosis of cholangiocarcinoma (CCA). Study Design: Experimental study. Place and Duration of the Study: Department of General Surgery, Zhenjiang Hospital, Zhenjiang Province, China, between January and March 2022. Methodology: Available datasets were obtained from the Gene Expression Omnibus (GEO) database to construct the train cohort and the test cohort of CCA, and screened out the di erentially expressed genes (DEGs) of CCA. Next, an ANN model for CCA diagnosis was constructed based on the scores of the DEGs and evaluated its accuracy and e ciency using ROC curves. Finally, the immune in ltration and the function of extracellular matrix (ECM) protein SPACRL1 were analysed to reveal the characteristic alterations in CCA. Results: This analysis revealed 166 DEGs, mainly concentrated in the ECM organisation, neutrophil activation and other pathways. Then a set of 17 CCA disease signature genes scores were obtained to build an ANN prediction model and the ROC curve was plotted. The AUC in the train group (0.980) indicated that the accuracy of the diagnosis model is extremely high. Finally, there was a signi cant increase of B cells naïve (p=0.025), tregs (p=0.004), and macrophages M1 (p<0.001) in the tumour-microenvironment of CCA, while SPARCL1 was a protective factor on disease-speci c survival (DSS) in CCA (p=0.009). Conclusion: This study has developed an accurate prediction model for CCA diagnosis, and identi edSPARCL1 as pivotal factor in CCA by modulating the tumour immune-microenvironment.
Keywords:
Bioinformatics
,
cholangiocarcinoma
,
Prognosis model
,
Arti cial neural network
,
SPARCL1
,
Immune microenvironment