Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection

Author:

Sun Shuo1,Cai Xiaoni2,Shao Jinhai2,Zhang Guimei3,Liu Shan4,Wang Hongsheng1

Affiliation:

1. Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Beihua University, Beihua University, Jilin 132013, China

2. Department of General Surgery, Shangyu People's Hospital of Shaoxing, the Second Affiliated Hospital of Zhejiang University Medical College Hospital, Shaoxing 312399, China

3. Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Jilin University, Changchun 130061, China

4. Department of Nuclear Medicine, The Second Hospital of Jilin University, Jilin University, Changchun 130041, China

Abstract

<abstract> <p>The association between adhesion function and papillary thyroid carcinoma (PTC) is increasingly recognized; however, the precise role of adhesion function in the pathogenesis and prognosis of PTC remains unclear. In this study, we employed the robust rank aggregation algorithm to identify 64 stable adhesion-related differentially expressed genes (ARDGs). Subsequently, using univariate Cox regression analysis, we identified 16 prognostic ARDGs. To construct PTC survival risk scoring models, we employed Lasso Cox and multivariate + stepwise Cox regression methods. Comparative analysis of these models revealed that the Lasso Cox regression model (LPSRSM) displayed superior performance. Further analyses identified age and LPSRSM as independent prognostic factors for PTC. Notably, patients classified as low-risk by LPSRSM exhibited significantly better prognosis, as demonstrated by Kaplan-Meier survival analyses. Additionally, we investigated the potential impact of adhesion feature on energy metabolism and inflammatory responses. Furthermore, leveraging the CMAP database, we screened 10 drugs that may improve prognosis. Finally, using Lasso regression analysis, we identified four genes for a diagnostic model of lymph node metastasis and three genes for a diagnostic model of tumor. These gene models hold promise for prognosis and disease diagnosis in PTC.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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