Department of Biotechnology, Delhi Technological University, Delhi, India, 110042.
Department of Biotechnology, Delhi Technological University, Delhi, India, 110042.
Comput Biol Med. 2022 Jul;146:105505. doi: 10.1016/j.compbiomed.2022.105505. Epub 2022 Apr 17.
Non-melanoma skin cancers (NMSCs) are the fifth most common type of cancer worldwide, affecting both men and women. Each year, more than a million new occurrences of NMSC are estimated, with Squamous Cell Carcinoma (SCC) representing approximately 20% of all skin malignancies. The purpose of this study was to find potential diagnostic biomarkers for SCC by application of eXplainable Artificial Intelligence (XAI) on XGBoost machine learning (ML) models trained on binary classification datasets comprising the expression data of 40 SCC, 38 AK, and 46 normal healthy skin samples. After successfully incorporating SHAP values into the ML models, 23 significant genes were identified and were found to be associated with the progression of SCC. These identified genes may serve as diagnostic and prognostic biomarkers in patients with SCC.
非黑色素瘤皮肤癌（NMSC）是全球第五大常见癌症类型，影响男性和女性。据估计，每年有超过 100 万例新的 NMSC 发生，其中鳞状细胞癌（SCC）约占所有皮肤恶性肿瘤的 20%。本研究旨在通过在基于二元分类数据集的 XGBoost 机器学习（ML）模型上应用可解释人工智能（XAI），找到 SCC 的潜在诊断生物标志物。这些数据集包含 40 例 SCC、38 例 AK 和 46 例正常健康皮肤样本的表达数据。在成功将 SHAP 值纳入 ML 模型后，确定了 23 个与 SCC 进展相关的显著基因。这些鉴定出的基因可能作为 SCC 患者的诊断和预后生物标志物。
