Immunohistochemistry and machine learning study of DNA replication-associated proteins in uterine epithelial tumors and precursor lesions

Endometrioid adenocarcinoma (EA) has been on the increase in recent years in developed countries. Early detection of endometrioid adenocarcinoma in the endometrial corpus is crucial for patient prognosis and early treatment, although their distinction can sometimes be challenging. In this study, we focused on DNA replication-related proteins through immunohistochemical analysis and investigated whether the discrimination between EA and their precursor lesions is achievable using machine learning techniques. The research utilized tissue specimens from 100 cases, including EA of different grades (Grade 1; G1, Grade 2; G2, Grade 3; G3) and their precursor lesions (endometrial hyperplasia without atypia; EH, endometrial atypical hyperplasia: AH). Immunohistochemical analysis of DNA replication-related proteins, such as ORC1, Cdt1, Cdc6, MCM7, Cdc7, and Geminin, was conducted for each case, measuring the Labeling Index (LI) and optical density (OD) of protein expression. Furthermore, we performed statistical significance tests and machine learning -discriminant analysis using LI and OD as inputs, employing non-linear Support Vector Machines (NSVM). The NSVM discriminant analysis demonstrated the accuracy of over 85 % between EH and each differentiation grade of EA, the accuracy is also similar for AH and each differentiation grade of EA. In addition, changing the combination of DNA replication-related proteins used for discrimination resulted in a high accuracy (95-100 %). A discriminant analysis with NSVM using the LI and OD of DNA replication-related proteins may enable the differentiation of EA from its precursor lesions.