Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis.

ABSTRACT:

Objective

The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients.

Methods

Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC nonbone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed

SUBMITTER: Fan TD 

PROVIDER: S-EPMC9424032 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature