Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome.

ABSTRACT:

Introduction

Proteomics technology has been used in various fields in recent years for the Q6 exploration of novel markers and the study of disease pathogenesis, and has become one of the most important tools for researchers to explore unknown areas. However, there are fewer studies related to the construction of clinical models using proteomics markers.

Methods

In our previous study we used DIA proteomics to screen for proteins that were significant in 31 PCOS patients compared to women of normal reproductive age. In this study, we used logistic regression among these protein markers to screen out variables with diagnostic value and constructed logistic regression models.

Results

We constructed a logistic model using these protein markers, where HIST1H4A (OR=1.037) w

SUBMITTER: Tong C 

PROVIDER: S-EPMC10579946 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature