Four‐gene signature based on machine learning filtration could predict prognosis of patients with breast cancer. Issue 2 (31st October 2022)

• Record Type: Journal Article
• Authors: Liu, Bo; Wang, Huina; Wang, Xin; Long, Junqi; Zhuang, Xujie; Ji, Xinchan; Zhu, Nian; Li, Jinmeng; Gao, Ting; Zhang, Xuehui; Yu, Jiangyong; Zhao, Shuangtao
• Other Names: Yu Hui guestEditor.
• Is Part Of: Expert systems. Volume 40:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-31
• Subjects: breast cancer -- predicative model -- prognosis -- signalling pathway; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.13157
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Background: This study aims to propose a breast cancer prediction model for early diagnosis and prognosis management of breast cancer. Objective: In order to explore the pathogenesis of breast cancer and develop accurate breast cancer screening and treatment methods, we have used machine‐learning technologies to conduct an in‐depth study of breast cancer genetic data to obtain new breast cancer signature and prognostic prediction models. Methods: We explored an optimal cluster by unsupervised clustering methods with different expression genes (DEGs) between normal ( n = 113) and tumour ( n = 1, 102) samples. Using least absolute shrinkage and selection operator (LASSO) regression, we selected four biomarkers to develop a predictive model by Cox regression method in the training set ( n = 1, 083) and validated its predictive accuracy and independence in the testing sets ( n = 2, 480). Then Gene Set Enrichment Analysis (GSEA) revealed enriched biological pathways in clusters. Finally, we constructed a nomogram including this signature and other significant risk factors to predict survival rates in patients. Results: Four mRNAs ( CD163L1, QPRT, NKAIN1 and TP53AIP1 ) between two clusters from 4, 938 DEGs were identified, and then a four‐gene model (risk scores = 0.454*CD163L1–0.360*NKAIN1 + 0.581*QPRT + 0.788*TP53AIP1 ) was established to divide patients into high‐ and low‐risk group with significantly different prognosis ( p < 0.0001) in the training set. Integrated analysis revealed dysregulated molecular processes including predominantly oncogenic signalling pathway, cell cycle and DNA repair in high‐risk group but enriched metabolism pathway in low‐risk group. In addition, this model had similar predictive value (HR >1.60; p < 0.05) in three independent validation sets, which could predict survival independently with more power compared with single clinical factor. In addition, the nomogram could predict the prognosis of breast cancer patients precisely in the training set and another three testing sets. Conclusion: This model could predict prognosis of breast cancer patients precisely and independently, and provide evidence to make treatment decisions and design clinical trials.