• J Coll Physicians Surg Pak · May 2023

    A Cholangiocarcinoma Prediction Model Based on Random Forest and Artificial Neural Network Algorithm.

    • Jianhua Liao, Chunyan Meng, Baoqing Liu, Mengxia Zheng, and Jun Qin.
    • Department of General Surgery, Zhejiang Hospital, Hangzhou, China.
    • J Coll Physicians Surg Pak. 2023 May 1; 33 (5): 578586578-586.

    ObjectiveTo construct a prognostic model using artificial neural network (ANN) approach, providing an idea for the prediction and diagnosis of cholangiocarcinoma (CCA).Study DesignExperimental study. Place and Duration of the Study: Department of General Surgery, Zhenjiang Hospital, Zhenjiang Province, China, between January and March 2022.MethodologyAvailable 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 differentially 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 efficiency using ROC curves. Finally, the immune infiltration and the function of extracellular matrix (ECM) protein SPACRL1 were analysed to reveal the characteristic alterations in CCA.ResultsThis 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 significant 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-specific survival (DSS) in CCA (p=0.009).ConclusionThis study has developed an accurate prediction model for CCA diagnosis, and identified SPARCL1 as pivotal factor in CCA by modulating the tumour immune-microenvironment.Key WordsCholangiocarcinoma, Artificial neural network, Immune microenvironment, Bioinformatics, Prognosis model, SPARCL1.

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