Introduction
As the leading cancer diagnosis in women, breast cancer accounted for approximately 2,261,000 new cases and 684,000 fatalities in 2020 [
1]. This hormone-dependent malignancy primarily affects the mammary gland in females. Accurate identification of menopausal status is vital for effective prevention, detection, and treatment [
2,
3]. A population-based study investigating the impact of premenopausal and postmenopausal breast cancer revealed that the mortality rate of patients with postmenopausal breast cancer in 2018 was 3.7 times greater than that in patients with premenopausal breast cancer [
4]. Given the unique molecular characteristics of these two conditions, personalized strategies are required to manage breast cancer based on menopausal status. For instance, endocrine therapy, which reduces estrogen or progesterone levels, is recommended for postmenopausal patients with estrogen receptor (ER) or progesterone receptor (PR) positivity but is unsuitable for premenopausal patients [
5,
6]. It has been widely recognized that menopausal status is associated with estrogen, progesterone, and other sex hormone levels, potentially influencing the activity of ER, PR and many other signaling pathways participating in the initiation and progression of breast cancer. However, the intricate molecular distinctions between premenopausal and postmenopausal breast cancer remain opaque. This gap in understanding impedes the full realization of precision medicine tailored to menopausal status. Therefore, enhancing our understanding of the unique molecular mechanisms of breast cancer through gene expression profile analyses is essential to improve early detection, diagnosis, and treatment strategies.
With the significant advancements in high-throughput technologies for genome-wide profiling of methylation events and gene expression levels, including methods such as methylation microarrays, MeDip-seq, and RNA-seq, and the availability of public datasets, we can now analyze data collected worldwide. Leveraging bioinformatic methods, we have the tools to identify potential biomarkers and pathways linked to menopausal status. However, numerous challenges arise in the integration and analysis of datasets from different sources. Fortunately, improvement in the differential expression analysis method enables us to perform cross-study analysis. In recent years, various differential expression analysis methods have been proposed, providing a variety of tools to ensure the robustness of our research findings.
To date, large-scale bioinformatic studies focusing on the differentially expressed genes (DEGs) associated with menopause in breast cancer patients have been scarce. The primary objective of our study is to illuminate the molecular distinctions between premenopausal and postmenopausal breast cancer patients. In our study, we attempted to collect more datasets to increase the sample size. In an integrated large cohort, we performed differential expression analyses to identify DEGs using two different algorithms. Additionally, Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed. In addition, protein–protein interaction (PPI) networks were constructed to further elucidate the direct and indirect associations between the DEGs. In doing so, we hope to pinpoint key menopause-related biomarkers that could prove instrumental in future breast cancer research. Furthermore, understanding these biomarkers will undoubtedly shed light on the disease’s pathogenesis, offering new avenues for clinical drug development and therapeutic interventions.
Discussion
In this study, we investigated the differential gene expression between premenopausal and postmenopausal breast cancer patients by analyzing eight breast cancer datasets comprising 693 samples. We aimed to enhance the reliability of our analysis results by employing two different algorithms. As a result, we identified 762 DEGs that exhibited significant differences between the two groups. Among these, multiple genes have been well clarified to be associated with tumour initiation and progression. These include Matrix Metallopeptidase 7 (MMP7), transcript factors of YAP1 (one of the most important effectors of the Hippo pathway) and FOXM1, fibroblast growth factor receptor 2 (FGFR2), Eukaryotic initiation factor 3B (EIF3B), Kinesin Family Members (Kif14, Kif4A, Kif23 and Kif2C), Cyclin Dependent Kinase 1 (CDK1), Cell division cycle proteins (CDCA3, CDCA5, CDCA7, CDCA8, CDCA20 and CDC25C) and Check point Kinase 1 (CHEK1). Some of these genes have also been found to be associated with breast cancer metastasis in our previous research [
25].
Among the top enriched pathways, the p53 signaling pathway and Hippo pathway are particularly remarkable, because they are involved in various intracellular regulations, including cellular senescence, energy metabolism regulating and blocking metastasis. The p53 signaling pathway, crucial in tumorigenesis [
26], is frequently mutated in various human tumors, leading to a loss of its inhibitory effect on tumor growth. In this report, CDKN2A, a gene within the p53 pathway, is involved in p53-dependent cellular senescence, proliferation, and apoptosis, while it may be a pioneering prognostic predictor for breast cancer [
27,
28]. Furthermore, Cyclin D1 phosphorylates Rb by binding to cyclin-dependent kinase (CDK) 4/6, resulting in activation of E2F transcription and cell cycle transition from her G1 phase to S phase. The tumor tumor-suppressive role of SERPINB5 in breast cancer is also supported by experimental evidence [
29]. On the other hand, the Hippo pathway, originally discovered in
Drosophila melanogaster as a crucial regulator of tissue development, is involved in tumorigenesis by regulating cell proliferation and apoptosis. For example, aberrations in the Hippo pathway and YAP/TAZ-TEAD activity are closely related to various human cancers, while targeting the Hippo pathway for treatment remains a compelling challenge [
30].
Of particular interest, four genes (TYMS, GART, ABCC3, and GGH) were notably found to be associated with folate metabolism and involved in antifolate resistance. To date, antifolates targeting folate metabolism have played a crucial role in the treatment of malignant tumors. Various antifolates, such as the 4-amino folic acid analogue aminopterin, its homologue 4-amino-10-methylfolic acid (methotrexate), raltitrexed (Tomudex; ZD1694), and pemetrexed (Alimta; MTA, LY231514), have been discovered and introduced into oncology clinics for the chemotherapeutic treatment of childhood acute lymphoblastic leukemia, colorectal cancer, malignant pleural mesothelioma, and non-small cell lung cancer [
31‐
35].
Raltitrexed and pemetrexed selectively inhibit glycinamide ribonucleotide transformylase (GART) and thymidylate synthase (TYMS), which are crucial for the de novo biosynthesis of purine and thymidine nucleotides, respectively. These antifolates have been introduced for the treatment of malignant tumors. ATP-binding cassette sub-family C member 3 (ABCC3, also known as MRP3), a member of the ATP-driven multidrug resistance (MDR) transporters, mediates the efflux of folates and hydrophilic antifolates. Gamma-glutamyl hydrolase (GGH) catalyzes the removal of gamma-linked polyglutamates from (anti)folylpolygamma-glutamates. Additionally, a recent study has shown that the expression level of GGH is associated with poor prognosis and unfavorable clinical outcomes in invasive breast cancer [
36]. We believe that the association between these four genes and antifolates represents one of multiple pathways that could potentially act in both premenopausal and postmenopausal breast cancer.
Further KEGG pathway enrichment analysis based on the PPI subnetwork provided additional information. The first cluster was significantly associated with several important pathways, including the cell cycle, oocyte meiosis, and progesterone-mediated oocyte maturation pathways. The cell cycle is fundamental to the growth and development of all organisms and plays a significant role in cancer development and progression. For example, dysregulation of the cell cycle is a hallmark of cancer, and many chemotherapeutic drugs exert their effects by targeting the cell cycle machinery [
37]. We identified several DEGs involved in the cell cycle, including CDK1, CHEK1, CDC25C, BUB1, CDC20, and TTK, which not only are related to breast cancer patient survival but also have existing targeted drugs. However, none have been reported in association with menopause. How these genes affect premenopausal and postmenopausal breast cancer has not yet been fully demonstrated. Further study of these genes related to the cell cycle pathway will help us understand the mechanism of breast cancer for different menopausal statuses and strengthen the potential utility of these genes as therapeutic targets. In addition, CDK1 and CHEK1 are involved in the p53 signaling pathway, indicating the potential effect of menopausal status on the activity of p53 signaling.
Consistent with the key role of menopause in our study, we observed that DEGs involved in oocyte meiosis and progesterone-mediated oocyte maturation, two pathways closely associated with reproductive aging and cessation, also emerged as significant in our analysis. It is widely accepted that women’s hormonal milieu undergoes significant changes during menopause, with potential implications for breast cancer biology [
38]. Previous studies have reported the association of these pathways with breast cancer [
39‐
41]. In addition to CDC25C, BUB1, and CDK1 mentioned above, AURKA, which plays a role in both pathways, is linked to survival and has targeted drugs. Importantly, AURKA has been found to be associated with an increased risk of invasive breast cancer among postmenopausal women [
42].
The second cluster of DEGs, including FGFR2, KDR2 and MET, indicates the importance of key cancer-related pathways, including the PI3K-Akt signaling pathway, EGFR tyrosine kinase inhibitor resistance pathway, Rap1 signaling pathway, Ras signaling pathway, and MAPK signaling pathway. A few studies have reported associations of these pathways with breast cancer. In addition, drugs targeting these signaling pathways are available. For the first time, our study reveals a connection between these signaling pathways and menopausal status, laying the groundwork for future clinical development of breast cancer treatment strategies that cater to women with different menopausal statuses. Among these DEGs, KDR and MET are linked to survival and have available targeted drugs. Therapies targeting these key genes may be effective in improving patient outcomes. Additionally, one GWAS presented solid evidence of a strong association between the FGFR2 locus and ER status in breast cancer patients [
43]. Another study found that menopause has a greater impact on ER- than ER+ breast cancer incidence [
44]. These findings, along with ours, hint at the relationship between breast cancer, menopausal status, and ER status.
Interestingly, the Cluster 6 genes involved in PPAR signaling and adipose metabolism showed different expression between premenopausal and postmenopausal breast cancer patients. It has been well established that after menopause, lower levels of estrogen can lead to the accumulation of fat around the waist instead of the hips and thighs. For postmenopausal women, abdominal fat makes up 15 to 20% of their total body weight, compared to 5 to 8% in premenopausal women [
45]. This also validates the reliability of our differential expression analysis results. Notably, adiposity is a risk factor for developing breast cancer in postmenopausal women, as breast fat has a major role in the genesis and progression of breast cancer. Rose et al. argued that obese postmenopausal women have an increased breast cancer risk, the principal mechanism for which is elevated estrogen production by adipose tissue [
46]. Our analysis showed that DEGs (CD36, FABP4, SLC27A6, PPARA) enriched in the PPAR signaling pathway were all strongly associated with patient survival. However, whether menopause-associated obesity affects the initiation and progression of breast cancer remains an open question.
Additionally, many chemokines or cytokines, such as CCL20, CXCL5, and CXCL13 (Cluster 9), had significantly different expression levels between the two populations, which indicates differences in the tumor microenvironment. This difference could lead to a change in the infiltration of immune cells in tumor tissues and affect the efficacy of immune treatment. Locally produced and systemic cytokines are likely to affect breast cancer growth and behavior [
47].
Compared with previous studies, our research benefits from a larger sample size and the use of two different algorithms to enhance the robustness of the results. In addition, the MAMA algorithm allows us to analyze data from different geographic regions. The studies included in our analysis encompass samples not just from the United States but also from Germany, France, and Belgium. This geographical diversity ensures a more global representation. However, this study has several limitations. First, some subsets lacked crucial clinical information, preventing us from analyzing the effect of clinical factors on gene expression across the entire cohort, even though we understand that some clinical factors, such as age and race, might affect menopausal status or gene expression. Second, despite using two algorithms to bolster the robustness of our results, it was challenging to determine whether we overlooked an essential gene due to algorithm differences. Third, it would be preferable to have an independent validation set. Therefore, we are attempting to collect our own clinical samples and pay more attention to these points mentioned above in our future studies. Other databases, such as TCGA, are also valuable resources for cancer research [
48‐
50], but we did not use them in this study because they did not meet the requirements of the MAMA algorithm.
In conclusion, we utilized two differential expression analysis methods to identify several DEGs associated with menopausal status in a large integrated cohort. The interactions of the DEGs were depicted through PPI networks. Furthermore, we identified several key pathways. Most of our results related to menopausal status are reported for the first time; thus, these findings could provide a valuable reference for treating patients with premenopausal and postmenopausal breast cancer. Understanding the DEGs between premenopausal and postmenopausal breast cancer and elucidating their roles in the development and progression of the disease can offer valuable insights into its underlying mechanisms. Further studies are needed to comprehensively investigate this relationship and uncover the specific mechanisms involved. Continued research in this area will help improve our understanding of breast cancer and potentially lead to the development of more effective treatments tailored to the specific needs of premenopausal and postmenopausal patients.
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