Menstrual Phase and Menopausal Status Classification of Benign Breast Tissue Using Hormone-Regulated Gene Expression...

Area of Science:

• Oncology
• Genomics
• Biomarker Discovery

Background:

• Validating breast cancer risk biomarkers in benign breast samples (BBS) is challenging due to menstrual phase (MP) and menopausal status (MS) variations.
• Previous work identified hormone-related gene expression and histomorphology for BBS classification by MS/MP.
• This study aimed to validate prior findings by evaluating both gene expression and histomorphology together.

Purpose of the Study:

• To validate the combined use of gene expression and histomorphology for classifying benign breast samples by menopausal status and menstrual phase.
• To assess the predictive accuracy of specific gene sets and histomorphological parameters for MS and MP.
• To facilitate the development of reliable breast cancer risk biomarkers.

Main Methods:

• Benign breast samples (BBS) from 131 women (86 premenopausal, 55 postmenopausal) were analyzed.
• Gene expression of luteal phase (LP) and menopausal genes was measured using RT-qPCR.
• Histomorphology parameters were used to classify premenopausal samples into LP or non-LP.
• Logistic regression and ROC analysis determined the predictive accuracy (AUC) for MP/MS.

Main Results:

• A five-gene set plus age > 50 accurately predicted menopausal status (MS) with an AUC of 0.93.
• In premenopausal women, high TNFSF11 expression predicted non-LP vs. LP (AUC 0.80).
• Adding histomorphology improved LP prediction in reduction mammoplasty (RM) samples (AUC 0.95) but not contralateral unaffected breast (CUB) samples (AUC 0.84).

Conclusions:

• A five-gene signature accurately predicts menopausal status and menstrual phase in benign breast samples.
• This predictive capability is crucial for developing breast cancer risk biomarkers.
• The findings support the use of archived sample repositories for biomarker development.