Academic Journal
Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection
| العنوان: | Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection |
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| المؤلفون: | Shuo Sun, Xiaoni Cai, Jinhai Shao, Guimei Zhang, Shan Liu, Hongsheng Wang |
| المصدر: | Mathematical Biosciences and Engineering, Vol 20, Iss 12, Pp 20599-20623 (2023) |
| بيانات النشر: | AIMS Press, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Biotechnology LCC:Mathematics |
| مصطلحات موضوعية: | adhesion, bioinformatics, immune cell infiltration, machine learning, papillary thyroid carcinoma, Biotechnology, TP248.13-248.65, Mathematics, QA1-939 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1551-0018 |
| Relation: | https://doaj.org/toc/1551-0018 |
| DOI: | 10.3934/mbe.2023911?viewType=HTML |
| DOI: | 10.3934/mbe.2023911 |
| URL الوصول: | https://doaj.org/article/2e9fca8dc6d14a41923899b82cc395ef |
| رقم الانضمام: | edsdoj.2e9fca8dc6d14a41923899b82cc395ef |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 15510018 |
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| DOI: | 10.3934/mbe.2023911?viewType=HTML |