Targeted next-generation sequencing reveals genomic differences between male and female breast cancer

Introduction

Breast cancer, traditionally perceived as a predominantly female malignancy, arises from gene mutations in mammary epithelial cells under various carcinogenic influences. While it is uncommon for men, who also possess breast tissue, to develop breast cancer, the condition is not without precedent. Male breast cancer (MBC) is a rare yet significant disease, accounting for about 1% of all breast cancer cases globally (1,2). Despite its infrequency, MBC exhibits distinctive features that set it apart from female breast cancer (FBC), underscoring the need for tailored clinical management approaches.

The existing literature underscores the unique aspects of MBC. Zhao et al. (3) provided an in-depth analysis of patient and tumor characteristics associated with MBC survival, revealing a need for gender-specific prognostic indicators. Lei et al. (4) examined the clinical characteristics and prognostic factors of MBC in China, further emphasizing the importance of tailored treatment strategies. Spreafico et al. (5) and Yalaza et al. (6) have shed light on MBC-specific risk factors, including family history and genetic predispositions, underscoring the imperative for a deeper investigation into the characteristics of MBC. However, the majority of these studies have concentrated primarily on the clinical manifestations of MBC, often overlooking the molecular characteristics. This oversight highlights a significant gap in the current understanding of MBC, particularly in the realm of molecular biology, which is pivotal for advancing diagnostic and therapeutic strategies in the field.

With the rising incidence of MBC globally (1), the exploration of the molecular mechanisms of MBC has intensified. Gucalp et al. (7) and Valentini et al. (8) emphasized the biological differences between MBC and FBC. Moelans et al. (9) delivered a comprehensive comparative analysis of the mutational landscapes of MBC and FBC, utilizing targeted capture followed by massively parallel sequencing. Their work accentuated the distinct molecular signatures between the two forms of cancer, suggesting that these intrinsic differences might necessitate divergent clinical management approaches. These insightful contributions have significantly enriched the field, providing a foundation for more tailored and precise treatment strategies for MBC patients.

Building upon previous studies, our study delved into the genomic disparities between Chinese patients with MBC and FBC through the application of targeted next-generation sequencing technology. Our study comparatively analyzed the differences in clinical features, molecular features and conducted functional enrichment analysis [Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)] between MBC and FBC patients. This multifaceted study is essential for uncovering the subtle molecular intricacies of MBC, which, despite its rarity, is critical for advancing our understanding of the disease.

Our single-center study, though limited by a smaller cohort size due to the scarcity of MBC cases, presented an in-depth and Chinese population-specific perspective that is invaluable. By providing a gender-specific genomic viewpoint grounded in a meticulous analysis of our small but well-defined cohort, our research uncovered the unique molecular features of MBC. This discovery is instrumental in fostering new avenues for precision medicine, potentially leading to the development of personalized therapeutic strategies that could enhance the clinical outcomes for MBC patients. We present this article in accordance with the STREGA reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1105/rc).

Methods

Patients and cohorts

In this cross-sectional study, a total of 26 newly diagnosed and initially treated with resection breast cancer patients, comprising 12 males and 14 females, were enrolled from The First Affiliated Hospital of Harbin Medical University between May 2012 and July 2024. The general clinical characteristics (i.e., diagnosis age, gender, identification number) of the enrolled patients were collected. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was reviewed and approved by the Ethics Committee of The First Affiliated Hospital of Harbin Medical University (approval No. 202128). Prior to enrollment, written informed consent forms were collected from each patient or their representatives. All experimental procedures in this study were conducted in strict accordance with relevant regulatory guidelines. For confirmation of pathological type and histological grade, all cases underwent a thorough review by at least two pathologists. The histological grade of breast cancer was determined according to the Nottingham modification of the Scarff-Bloom-Richardson (SBR) grading system (10). Additionally, we obtained RNA-Seq data of MBC and FBC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to conduct transcriptome-level research on MBC. The TCGA-BRCA dataset comprised 826 FBC and 11 MBC, while the GSE104730 dataset included 46 MBC.

Hematoxylin-eosin (HE) staining and immunohistochemical (IHC) staining

The resected tumor tissues of each patient were fixed in 10% neutral buffered formalin and embedded in paraffin. Four-micron-thick slices were made using HistoCore BIOCUT (Leica, Nussloch, Germany). HE staining of pathological tissue sections was performed using a VENTANA HE600 automatic staining platform (VENTANA HE600 System, Ventana Medical Systems, Tucson, AZ, USA). IHC staining was performed using BenchMark ULTRA autostainer, version 12.3 (Ventana Medical Systems, Guangzhou, China). The experimental conditions and interpretation standards for the following antibodies were based on the manufacturers’ recommendations: estrogen receptor (ER) (SP1, Ventana Medical Systems, Tucson, AZ, USA), progesterone receptor (PR) (1E2, Ventana Medical Systems), human epidermal growth factor receptor 2 (HER2) (4B5, Ventana Medical Systems), and Ki67 (30-9, Ventana Medical Systems).

DNA extraction and library preparation

Methods for preparing DNA and constructing libraries have been described previously (11). Briefly, tumor cells in formalin-fixed paraffin-embedded (FFPE) samples were obtained by laser microdissection LMD6500 (Leica), and the number of enriched tumor cells in each sample was not less than 200. DNA was independently extracted from FFPE samples using the Qiagen DNA FFPE Tissue kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The purity and concentration of DNA were quantified using a Qubit 2.0 fluorometer (Invitrogen, Carlsbad, CA, USA) and Qubit dsDNA HS (high sensitivity) Assay Kit (Invitrogen). The total amount of DNA for initial library construction was not less than 400 ng. DNA was broken into fragments of 200–300 bp with a Covaris S2 ultrasound system (Covaris, Woburn, MA, USA). Then, a sequencing panel of 1,021 cancer-related genes was used for library construction (Table S1). The identifiable genome-wide DNA library was used for subsequent target sequence hybridization capture, library cyclization and machine use. The capture probe was designed to cover cancer-related genes that are frequently mutated in tumors.

DNA sequencing and data analysis

According to the manufacturer’s instructions, sequencing was performed on the Gene⁺ Seq-200 Sequencing System. After sequencing, the raw data in fastq format were filtered, and adaptor and low-quality reads were removed. The effective ratio of each library was shown in Table S2. Burrows-Wheeler Aligner (BWA; version 0.7.12-r1039) (12) was used to align the clean reads to the reference human genome (UCSC genome Browser, version hg19). RealDcaller and TNscope were used to identify somatic single nucleotide variants (SNVs) and insertions or deletions of small fragments (InDels). CNVkit (v0.96) (13) was used to detect somatic copy number variations (CNVs). NCSV2 was used to detect structural variations (SVs). The tumor mutational burden (TMB) was defined as the number of non-silent somatic mutations (non-synonymous single nucleotide variation “SNV”, indel, and splice, ±2) per mega-base (1 Mb) of coding genomic regions sequenced (1.03 Mb for this 1021-gene panel) (14). The upper quartile of TMB of all patients was defined as cutoff value (9 muts/Mb), and the patients were divided into TMB-high (TMB-H) and TMB-low (TMB-L) groups.

The mutational signatures in our study originated from the Catalogue of Somatic Mutations in Cancer (COSMIC) (15). It classified the point mutation of the genome according to the trinucleotide sequence. R package deconstructSigs (version 1.8.0) was used to infer the composition of known COSMIC mutational signatures using the COSMIC Mutational Signatures version 3.3 (16). Decomposition of mutational signatures was performed using deconstructSigs based on the set of 30 mutational signatures (‘signature.cosmic’) for samples with at least 20 somatic mutations. To increase robustness, the mutations for each sample were bootstrapped 100 times and the mean weights across these 100 iterations were used (17). The genes were analyzed by Gene Ontology (GO) (18) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (19). GO analysis was performed with the Blast2GO bioinformatics platform; the KEGG Orthology Based Annotation System (KOBAS 3.0) with default parameters was used to analyze KEGG pathways.

Bioinformatics analysis of TCGA and GEO

The TCGA-BRCA and GSE104730 datasets were obtained from the TCGA and GEO databases, respectively. The TCGA-BRCA dataset comprised 826 FBC and 11 MBC, while the GSE104730 dataset included 46 MBC. The acquired RNA-seq data underwent further bioinformatics analysis. To eliminate systematic biases from different platforms and sequencing batches, the ComBat method from the R package sva was used to correct for batch effects in the gene expression data. Differentially expressed mRNAs (DEmRs) between MBC and FBC were identified using DESeq2, with the criteria of |log₂FC| >1 and an adjusted P value <0.05. Visualization of DEmRs was implemented using the R package ggplot2. To functionally characterize the DEmRs, KEGG and GO enrichment analyses were conducted using the R package clusterProfiler. Gene Set Enrichment Analysis (GSEA) was also performed using the clusterProfiler package, with |normalized enrichment score (NES)| >1 and P value <0.05 set as the significance thresholds. The results were visualized via the GseaVis package. Genes associated with the breast cancer pathway (hsa05224) were retrieved via the R package KEGGREST and then intersected with the list of differentially expressed genes (DEGs). The resultant gene set was visualized as a Venn diagram with the ggvenn package. The protein–protein interaction (PPI) network was constructed based on the STRING database (https://string-db.org) and subsequently visualized in Cytoscape software (version 3.10).

Statistical analysis

Data were analyzed with SPSS 19.0 software (SPSS, Chicago, IL, USA) and R (version 4.3.1). Wilcoxon test and Fisher’s exact test were used to analyze continuous variables and categorical variables between two groups. All tests were two-tailed, and P<0.05 was considered statistically significant.

Results

Comparison of clinical and pathological characteristics between MBC and FBC patients

A total of 26 patients with breast cancer were enrolled in this study, including 12 males and 14 females. The clinical and pathological characteristics were summarized in Table 1. The median age at diagnosis was 59 years for males and 55 years for females. All patients were diagnosed with invasive ductal carcinoma and underwent breast cancer resection without any other treatment before surgery. There were no significant differences between MBC and FBC patients in tumor diameter, location, TNM stage, histological grading, lymph node status or HER2 status. In both groups, more than 60% of patients had negative lymph nodes, while more than 80% were HER2-positive. Histologic grade II was observed in 66.67% of MBC patients, compared to 42.86% of FBC patients. All MBC patients were ER positive while only half of the FBC patients were ER positive; the difference was statistically significant (P<0.01). Similarly, 100% of MBC patients were PR positive while this percentage was only 50% in FBC (P<0.01). In addition, only 8.33% of MBC patients had high Ki67 expression (i.e., the Ki67 positive cells accounted for more than 50%), whereas this percentage reached 35.71% in FBC patients, although this difference was not statistically significant (P=0.10).

Genomic variations between MBC and FBC patients

A total of 345 somatic variations were detected across all patients, including 235 SNVs in 139 genes, 105 CNVs in 60 genes and 5 fusions (online supplementary table: https://cdn.amegroups.cn/static/public/tcr-2025-1105-1.xlsx). The most commonly altered genes were PIK3CA (38.46%), MYC (34.62%), TP53 (34.62%), MLL3 (30.77%), FGFR1 (26.92%), ERBB2 (23.08%), GATA3 (23.08%) and GNAS (19.23%) (Figure 1A). These percentages represented the proportion of patients with somatic variations in the total population. Missense mutations predominated, constituting 80.43% of all variants (Figure 1B). SNV (59.71%) was the main variant type (Figure 1C), and among these SNVs, the C>T transition emerged as the predominant class, representing 50% of the total SNVs (Figure 1D). The overall median TMB was 4.42 mutations/Mb (Table S3), and no significant difference in TMB was observed between the MBC (2.77 mutations/Mb) and FBC patients (5.76 mutations/Mb, P>0.05) (Figure 1E).

Figure 1 Overview of somatic variation in MBC and FBC patients. (A) Somatic variants profiles of cancer-related genes detected in breast cancer. The column chart (top) showed the number of somatic variants per patient. The bar chart (on the left) represented the mutation prevalence of cancer-related genes (top 29), which was arranged in descending order of mutation prevalence. The TMB and gender of each patient were indicated at the bottom. (B) All samples were analyzed for variant classification. (C) All samples were analyzed for variant type. (D) All samples were analyzed for SNV class. (E) Comparison of TMB values between MBC and FBC groups. CNV, copy number variation; FBC, female breast cancer; MBC, male breast cancer; SNV, single nucleotide variant; TMB, tumor mutational burden.

A total of 139 genes exhibited SNVs or InDels, while 60 genes displayed CNVs. Intriguingly, 19 genes were found to harbor alterations in both SNVs/InDels and CNVs (Figure 2A). Upon comparing the somatic variation profiles between the MBC and FBC groups, we observed a subset of genes with shared point mutations (SNVs or InDels) and CNVs. Conversely, distinct somatic variations were observed in certain genes that were unique to either the MBC or FBC group, which we have designated as “private genes” (Table S4). To visually represent these findings, Venn diagrams were applied to illustrate the overlap and exclusivity of affected genes between the MBC and FBC groups (Figure 2B,2C).

Figure 2 Variant characteristics in patients with MBC and FBC. (A-C) Venn diagrams showed mutated genes (SNVs, small InDels and CNVs) in different groups. (D-I) Comparison of somatic variation between MBC and FBC. The bar chart showed the prevalence of mutated genes in the MBC and FBC groups. *, P<0.05. CNV, copy number variation; FBC, female breast cancer; MBC, male breast cancer; SNV, single nucleotide variant.

Comparison of mutation prevalence among shared genes revealed that the mutation prevalence of PIK3CA was significantly higher in FBC patients than in MBC group (P<0.05), while MLL3 showed a high mutation prevalence nearly 40% in both groups without a significant disparity (Figure 2D). In MBC patients, ASXL1, CTNNA1, EPHA2, ERG, and PTPRD were the most frequently mutated private genes, each at 17% (Figure 2E). Conversely, TP53 (64%), MED12 (21%), and SETD2 (21%) were the predominant private genes in FBC group (Figure 2F). For shared genes, MYC, GNAS, and FGFR1 were the most common in terms of CNVs, with no significant difference between MBC and FBC (Figure 2G). In MBC patients, CDKN2B and TERC were identified as the most prevalent private CNV genes, with a combined occurrence of 16.67%, as depicted in Figure 2H. In contrast, within the FBC group, the genes ERBB2 and CDK12 showed the highest prevalence of CNVs, each with a rate of 28.57% as illustrated in Figure 2I. These findings highlighted distinct mutational profiles between MBC and FBC.

PIK3CA, MLL3, MYC and TP53 with high mutation prevalence were selected for TMB analysis (Figure 3). Our analysis revealed no significant impact of mutations in these genes on TMB values between FBC and MBC groups. Furthermore, no notable difference in TMB was observed between patients with these gene mutations and those with wild-type, both in FBC and MBC cohorts.

Figure 3 Comparison of TMB in cancer-related genes between different groups. FBC, female breast cancer; MBC, male breast cancer; TMB, tumor mutational burden; WT, wild type.

COSMIC mutational signatures in MBC and FBC patients

To further explore the genomic characterization of breast cancer, we characterized the mutational signatures of MBC and FBC based on COSMIC database (16). Mutational signatures are displayed using a 96-substitution classification defined by the substitution class and the sequence context immediately 3' and 5' to the mutated base (15). We found that signature 19 was present in both MBC and FBC groups, although its function is not yet fully understood. MBC patients exhibited signatures 2 and 7 related to APOBEC and ultraviolet light (Figure 4A). A high percentage of signature 11 (associated with HRD), signature 9 (associated with polymerase η), signature 4 (associated with smoking), and signature 1A (associated with age) were present in FBC group (Figure 4B). In summary, these mutational signatures provided novel perspectives and hypotheses on the pathogenesis of MBC and FBC, shedding light on both intrinsic and extrinsic factors that may influence the progression of these distinct breast cancer subtypes.

Figure 4 Cluster analysis of somatic variation was performed between MBC and FBC. (A,B) Mutational signatures based on COSMIC database, corresponding to (A) MBC and (B) FBC patients. RepairSig.R2 was similar to COSMIC signatures. Mutational characteristics of known possible etiologies were annotated. (C) GO enrichment analysis of private genes of MBC. (D) GO enrichment analysis of private genes of FBC. (E) KEGG pathway analysis of private genes of MBC. (F) KEGG pathway analysis of private genes of FBC. COSMIC, Catalogue of Somatic Mutations in Cancer; FBC, female breast cancer; GO, Gene Ontology; HRD, homologous recombination deficiency; KEGG, Kyoto Encyclopedia of Genes and Genome; MBC, male breast cancer; UV, ultraviolet.

GO and KEGG analysis

Variations in the CDS (coding sequence) region of genes may lead to changes in gene function, prompting us to conduct GO and KEGG analyses on MBC and FBC private genes to elucidate their roles. MBC and FBC were enriched in 40 and 76 genes for analysis, respectively. Notably, MBC genes were enriched in biological processes like positive regulation of metabolic processes (GO:0009893), multicellular organism development (GO:0007275) and cell proliferation (GO:0008283) (Figure 4C). FBC genes were enriched in biological processes and cellular component such as positive regulation of cellular metabolic process (GO:0031325), macromolecular complex (GO:0032991) and regulation of transcription (GO:0006355) (Figure 4D). All GO enrichment pathways were detailed in the online supplementary table (https://cdn.amegroups.cn/static/public/tcr-2025-1105-2.xlsx).

KEGG analysis revealed that both MBC and FBC private genes were enriched in breast cancer, pathways in cancer and endocrine resistance. Additionally, the top two KEGG pathways enriched by MBC private mutant genes were the ErbB signaling pathway and PI3K-Akt signaling pathway (Figure 4E). While FBC private genes were enriched in MicroRNAs in cancer, EGFR tyrosine kinase inhibitor resistance, thyroid hormone signaling pathway and signaling pathways regulating pluripotency of stem cells (Figure 4F).

Identification of DEmRs between MBC and FBC at the transcriptomic level

A total of 2,247 DEmRs were obtained by RNA-Seq analysis, among which 244 were upregulated and 2003 were downregulated in MBC compared with FBC (Figure 5A). The expression density of DEmRs across samples and the expression heatmap of the top 10 up-/down-regulated genes (ranked by |log₂FC| value) were presented in Figure 5B.

Figure 5 Identification of DEmRs between MBC and FBC. (A) Volcano plot of DEmRs. (B) Heatmap of DEmRs. The figure consists of two parts: the upper section displays density heatmaps of DEmRs across samples; the lower section presents a gene expression heatmap, where the x-axis represents samples and the y-axis represents genes. The genes displayed in the heatmap are the top 10 upregulated and downregulated genes ranked by |log₂FC| values. DEmRs, differentially expressed mRNAs; FBC, female breast cancer; FC, fold change; MBC, male breast cancer.

Functional enrichment analysis of DEmRs (GO and KEGG)

To highlight the function of DEmRs, GO and KEGG analyses were performed. GO analysis revealed that the DEmRs were involved in multiple categories in molecular functions, cellular component, and biological processes (Figure 6A). The major biological processes enriched included regulation of immune effector process, positive regulation of cytokine production, leukocyte migration, regulation of cell–cell adhesion and response to molecules of bacterial origin. Cellular component involves the external side of plasma membrane, collagen-containing extracellular matrix, cornified envelope, monoatomic/monovalent ion channel complex and apical plasma membrane. Molecular function encompasses immune receptor activity, cytokine activity and cytokine receptor activity, along with heparin binding and gated channel activity. KEGG analysis performed on all DEmRs revealed that various pathways, such as Cytokine-cytokine Receptor Interaction, Viral Protein Interaction with Cytokine and Cytokine Receptor, Cell Adhesion Molecules, Hematopoietic Cell Lineage, and Rheumatoid Arthritis, etc., were significantly enriched (Figure 6B).

Figure 6 Characterization of DEmRs. (A) GO enrichment analysis. (B) KEGG pathway analysis of DEmRs. (C) GSEA enrichment analysis. The dark blue line represents the mTOR signaling pathway; the light blue line represents the metabolism of xenobiotics by cytochrome P450; the dark green line represents graft-versus-host disease; the light green line represents antigen processing and presentation; the pink line represents the T cell receptor signaling pathway. (D) Venn diagrams showed breast cancer pathway related genes. (E) The protein-protein interaction network of DEmRs involved in breast cancer pathways. Red represents upregulated (genes/processes); blue represents downregulated (genes/processes), and the color intensity corresponds to the degree of the change. DEmRs, differentially expressed mRNAs; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genome.

GSEA

A total of 11 pathways were significantly enriched by DEmRs, with the top five enriched pathways being: mTOR signaling pathway, antigen processing and presentation, graft-versus-host disease, metabolism of xenobiotics by cytochrome P450, and T cell receptor signaling pathway (Figure 6C). These findings suggest that these genes play important roles in immune response, disease pathogenesis, and metabolic detoxification processes.

PPI network analysis

Using the R package “KEGGREST”, genes associated with the breast cancer pathway (hsa05224) were retrieved and intersected with the differentially expressed gene set, resulting in the identification of 26 breast cancer pathway-related genes (Figure 6D). The STRING database was subsequently employed to construct a protein-protein interaction (PPI) network. PPI network analysis revealed that the network consists of 26 nodes and 202 edges. The top three key nodes were EGFR, WNT4, and FGF10 with degrees of 14, 13, and 12, respectively. Notably, FGF16 was the only gene that was up-regulated, with degrees of 10 (Figure 6E).

Discussion

Due to low incidence, limited number of cases, and relatively low level of concern, research progress in MBC has been relatively slow, leading to clinical management that often adopts the same treatment protocols as those used for FBC. In this study, we employed high-throughput sequencing technology and bioinformatics methods to explore the genomic and transcriptome differences between MBC and FBC, with the hope of providing a fresh perspective for the precision diagnosis and treatment of breast cancer patients, especially those with MBC.

Our findings indicated that MBC patients were diagnosed at an older age compared to FBC patients, which is consistent with the majority of studies (20-22), although this difference was not statistically significant in our research and may be related to the small size of the cohort. The latter diagnosis is one of the reasons contributing to the poorer prognosis of MBC. Additionally, a previous study suggested that the development pattern of MBC is similar to that of breast cancer in postmenopausal women (20). In both genders, the most common histological type of breast cancer is invasive ductal carcinoma, with an incidence rate exceeding 80% (23,24). In a study of 135 MBC patients, invasive ductal carcinoma reached 90% (9). In our study, all samples were of the histological type invasive ductal carcinoma, with no other types found, which may be related to the small sample size. In another study of 15 MBC patients, all had invasive carcinoma (8). Two-thirds of MBC patients had grade II histology, while the proportion of FBC patients with grade III histology (57.14%) was higher than that of grade II histology (42.86%), which may lead to differences in their prevention and treatment strategies (25). Although this discrepancy may be due to the small sample size. Our research demonstrated that the expression of ER and PR in MBC was significantly higher compared to FBC, corroborating previous findings (26,27). The proportion of MBC patients with positive for both ER and PR has reached 100% in our study. In a substantial cohort of 135 MBC patients, Moelans et al. (9) reported an ER positivity rate of 96% and a PR positivity rate of 66%. In contrast, a smaller cohort of 15 MBC patients studied by Valentini et al. (8) showed an ER positivity rate of 87% and a notably higher PR positivity rate of 93%. This suggested that the high ER positivity in MBC may not be influenced by the size of the cohort.

Our study employed sequencing analysis based on a DNA panel of 1,021 genes for both MBC and FBC patients, diverging from the smaller panels utilized in previous research (28-30), thereby enabling the acquisition of more comprehensive genomic information. Remarkably, TP53 mutation was not found in MBC. This finding is consistent with the study of 135 MBC patients, in which the TP53 mutation prevalence among MBC patients was as low as 3% (9). Similarly, in the study of 15 MBC patients, only one had a TP53 mutation (8). Additionally, the low mutation prevalence of TP53 was reported to be similar to luminal A of FBC (31).

Although mutation in PIK3CA occurred in both MBC and FBC, there were significant differences in mutation prevalence. In the study of 15 MBC patients, the PIK3CA mutation prevalence was slightly higher, at 40 percent (8).That is to say, the mutation prevalence of TP53 and PIK3CA was relatively low in MBC (Figure 2D). These results were in line with the majority of studies, but there were also contradictory conclusions (32,33). PIK3CA plays a pivotal role in regulating vital cellular processes such as proliferation, differentiation, and apoptosis in cancer cells. It is directly correlated with tumor size and degree of malignancy, making it an important target for the development of anti-tumor drugs. Alpelisib, a PI3K (phosphatidylinositol-3 kinase) inhibitor, effectively suppresses the activity of PIK3CA, inducing apoptosis and/or cell cycle arrest. It is indicated for the treatment of advanced or metastatic breast cancer patients with PIK3CA mutations who have been previously treated with endocrine therapy (including postmenopausal women and men) (34,35).

We also found that MLL3 exhibited a high mutation prevalence in both MBC and FBC. Histone methyltransferase MLL3 (also known as KMT2C) mutations are often associated with the occurrence and development of cancer (36,37). Most MLL3 mutations in breast cancer are protein junction mutations or gene deletions, potentially resulting in functional loss (38). Moreover, reduced MLL3 function may impede the efficacy of endocrine therapy. Consequently, MLL3 holds significant implications for tailoring treatment strategies in oncology (39).

BRCA1/2 mutations are crucial to the occurrence and progression of breast cancer. However, in our study, only one BRAC1 mutation was found in FBC patients and one BRAC2 mutation in MBC patients, and the latter was a nonsense mutation. The low prevalence of BRCA1/2 mutations in our FBC cohort might be related to the small sample size. According to the literature, the prevalence of pathogenic variants in BRCA2 ranges from 4% to 40%, while pathogenic variants in BRCA1 are less common, accounting for 1.2% to 4% of MBC cases (40-42). This is consistent with our findings.

In our study, 19 genes exhibited both SNVs and CNVs (Figure 2A), suggesting that they may be more active in breast cancer. Additionally, we identified co-amplification of ERBB2 and CDK12 in four FBC patients (online supplementary table: https://cdn.amegroups.cn/static/public/tcr-2025-1105-1.xlsx). Notably, studies have linked co-amplification of CDK12 with ERBB2 to resistance to lapatinib (43), which could potentially impact the treatment options and prognosis for these patients.

In MBC, many somatic mutant genes were enriched in the ErbB and PI3K-Akt signaling pathways (Figure 4E). The downstream target of PI3K-Akt is mammalian target of rapamycin (mTOR), and the downstream transcription factors of mTOR include HIF1α, c-Myc, FoxO and other key molecules. Alterations in the PI3K/AKT/mTOR pathway are particularly common in breast cancer, and its overactivation can promote uncontrolled cell proliferation, ultimately leading to tumorigenesis (44). ErbB family members and their ligands are frequently overexpressed, amplified or mutated in many cancers, making them important therapeutic targets. The ErbB family includes four members, ErbB1, ErbB2, ErbB3 and ErbB4, which are membrane receptor tyrosine kinases involved in HER signaling transduction, with various ligands capable of binding to them and resulting in PI3K/AKT activation (45). PI3K/AKT/mTOR pathway inhibitors are an important part of the current clinical treatment of ER+ metastatic breast cancer. In addition, studies have shown that everolimus can inhibit the growth and aggressiveness of breast cancer cells through the PI3K/AKT/mTOR signaling pathway (46). These key pathways involved in the development of breast cancer provide new insights for the treatment of MBC patients.

The primary limitation of this study is its small sample size, which may limit the precision of our characterization of Chinese MBC patients. Future work should involve multicenter collaboration to expand the scale of MBC research, increase sample size, and seek further validation. As this study is retrospective in nature, comprehensive collection and analysis of family history and follow-up data was not possible. If future opportunities for prospective studies arise, we hope that these two parameters can be incorporated into the analysis. Additionally, germline mutations in the AR gene are associated with an increased risk of MBC, with 70–90% of primary or metastatic MBC expressing ARs (40). The AR gene in MBC features a broad range of repetitive sequences (CAG repeats), a highly polymorphic region of glutamine repeats (47). However, our study did not assess or compare AR expression levels between MBC and FBC patients. We acknowledge this omission and recognize it as an important direction for future research. We intend to incorporate this variable in future studies to explore its potential role in gender differences.

Our comprehensive bioinformatics analysis of transcriptomic data from both TCGA and GEO databases has revealed significant differences in gene expression profiles between MBC and FBC. MBC is characterized by widespread gene downregulation, most notably a pervasive suppression of immune-related pathways (e.g., cytokine signaling and antigen presentation), suggesting it possesses a more immunosuppressive tumor microenvironment. This widespread transcriptional divergence suggests that MBC is not merely a rare variant of breast cancer but may possess a unique molecular landscape.

Furthermore, alterations in key pathways like mTOR signaling and metabolic processes also contribute to MBC pathogenesis. Protein-protein interaction network analysis identified EGFR, WNT4, and FGF10 as central hub genes. Most notably, FGF16 was the sole upregulated key gene within the breast cancer pathway, indicating a potentially unique driver role in MBC.

In summary, this study highlights the distinct immunosuppressive features of MBC and identifies novel potential therapeutic targets (e.g., FGF16). These findings suggest treatment strategies for MBC should be tailored to its unique molecular basis, which differs from FBC.

Conclusions

In this study, we compared MBC and FBC at the genome level and found that there was a gender effect in the genome of breast cancer. The differences at the genome level were often reflected in the differences in clinical biological characteristics and diagnosis and treatment plans. Our study provided a new understanding of genome research on breast cancer and provides a theoretical basis for the accurate diagnosis and individualized treatment of MBC and FBC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STREGA reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1105/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1105/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1105/prf

Funding: This study was supported by the Research and Innovation Fund of The First Affiliated Hospital of Harbin Medical University (No. 2025M19).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1105/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was reviewed and approved by the Ethics Committee of First Affiliated Hospital of Harbin Medical University (approval No. 202128). Prior to enrollment, written informed consent forms were collected from each patient or their representatives.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022. CA Cancer J Clin 2022;72:7-33. [Crossref] [PubMed]
2. Korde LA, Zujewski JA, Kamin L, et al. Multidisciplinary meeting on male breast cancer: summary and research recommendations. J Clin Oncol 2010;28:2114-22. [Crossref] [PubMed]
3. Zhao J, Wang B, Zhao J, et al. Male breast cancer: A closer look at patient and tumor characteristics and factors associated with survival. Thorac Cancer 2020;11:3107-16. [Crossref] [PubMed]
4. Lei H, Hua B, Mao Y, et al. Clinical characteristics and prognostic factors of male breast cancer in China. Front Oncol 2024;14:1362826. [Crossref] [PubMed]
5. Spreafico FS, Cardoso-Filho C, Cabello C, et al. Breast Cancer in Men: Clinical and Pathological Analysis of 817 Cases. Am J Mens Health 2020;14:1557988320908109. [Crossref] [PubMed]
6. Yalaza M, İnan A, Bozer M. Male Breast Cancer. J Breast Health 2016;12:1-8. [Crossref] [PubMed]
7. Gucalp A, Traina TA, Eisner JR, et al. Male breast cancer: a disease distinct from female breast cancer. Breast Cancer Res Treat 2019;173:37-48. [Crossref] [PubMed]
8. Valentini V, Silvestri V, Bucalo A, et al. Molecular profiling of male breast cancer by multigene panel testing: Implications for precision oncology. Front Oncol 2022;12:1092201. [Crossref] [PubMed]
9. Moelans CB, de Ligt J, van der Groep P, et al. The molecular genetic make-up of male breast cancer. Endocr Relat Cancer 2019;26:779-94. [Crossref] [PubMed]
10. Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 1991;19:403-10. [Crossref] [PubMed]
11. Lu J, Yu R, Liu R, et al. Genetic aberrations in Chinese pancreatic cancer patients and their association with anatomic location and disease outcomes. Cancer Med 2021;10:933-43. [Crossref] [PubMed]
12. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754-60. [Crossref] [PubMed]
13. Talevich E, Shain AH, Botton T, et al. CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput Biol 2016;12:e1004873. [Crossref] [PubMed]
14. Peng W, Li B, Li J, et al. Clinical and genomic features of Chinese lung cancer patients with germline mutations. Nat Commun 2022;13:1268. [Crossref] [PubMed]
15. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature 2013;500:415-21. [Crossref] [PubMed]
16. Sondka Z, Dhir NB, Carvalho-Silva D, et al. COSMIC: a curated database of somatic variants and clinical data for cancer. Nucleic Acids Res 2024;52:D1210-7. [Crossref] [PubMed]
17. Rosenthal R, McGranahan N, Herrero J, et al. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol 2016;17:31. [Crossref] [PubMed]
18. Conesa A, Götz S. Blast2GO: A comprehensive suite for functional analysis in plant genomics. Int J Plant Genomics 2008;2008:619832. [Crossref] [PubMed]
19. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30. [Crossref] [PubMed]
20. Zurrida S, Nolè F, Bonanni B, et al. Male breast cancer. Future Oncol 2010;6:985-91. [Crossref] [PubMed]
21. Giordano SH. Breast Cancer in Men. N Engl J Med 2018;378:2311-20. [Crossref] [PubMed]
22. Wang X, Liu S, Xue Y. Clinicopathological features and prognosis of male breast cancer. J Int Med Res 2021;49:3000605211049977. [Crossref] [PubMed]
23. Cardoso F, Bartlett JMS, Slaets L, et al. Characterization of male breast cancer: results of the EORTC 10085/TBCRC/BIG/NABCG International Male Breast Cancer Program. Ann Oncol 2018;29:405-17. [Crossref] [PubMed]
24. Humphries MP, Sundara Rajan S, Honarpisheh H, et al. Characterisation of male breast cancer: a descriptive biomarker study from a large patient series. Sci Rep 2017;7:45293. [Crossref] [PubMed]
25. Thuler LC, Bergmann A. Male breast cancer: clinical-epidemiological characteristics of 1189 Brazilian patients. Aging Male 2015;18:118-23. [Crossref] [PubMed]
26. Sabel MS. Breast Cancer in Special Populations. In: Essentials of Breast Surgery. Philadelphia, PA: Mosby Elsevier; 2009:323-33.
27. Giordano SH, Cohen DS, Buzdar AU, et al. Breast carcinoma in men: a population-based study. Cancer 2004;101:51-7. [Crossref] [PubMed]
28. Tedaldi G, Tebaldi M, Zampiga V, et al. Male Breast Cancer: Results of the Application of Multigene Panel Testing to an Italian Cohort of Patients. Diagnostics (Basel) 2020;10:269. [Crossref] [PubMed]
29. Rizzolo P, Zelli V, Silvestri V, et al. Insight into genetic susceptibility to male breast cancer by multigene panel testing: Results from a multicenter study in Italy. Int J Cancer 2019;145:390-400. [Crossref] [PubMed]
30. Vogel KJ, Postula LM, Andolina KM, et al. The role of multi-gene hereditary cancer panels in male patients with breast cancer. Cancer Res 2018;78:PD7-11.
31. Deb S, Wong SQ, Li J, et al. Mutational profiling of familial male breast cancers reveals similarities with luminal A female breast cancer with rare TP53 mutations. Br J Cancer 2014;111:2351-60. [Crossref] [PubMed]
32. Piscuoglio S, Ng CK, Murray MP, et al. The Genomic Landscape of Male Breast Cancers. Clin Cancer Res 2016;22:4045-56. [Crossref] [PubMed]
33. Comprehensive molecular portraits of human breast tumours. Nature 2012;490:61-70. [Crossref] [PubMed]
34. André F, Ciruelos E, Rubovszky G, et al. Alpelisib for PIK3CA-Mutated, Hormone Receptor-Positive Advanced Breast Cancer. N Engl J Med 2019;380:1929-40. [Crossref] [PubMed]
35. Ellis H, Ma CX. PI3K Inhibitors in Breast Cancer Therapy. Curr Oncol Rep 2019;21:110. [Crossref] [PubMed]
36. Kandoth C, McLellan MD, Vandin F, et al. Mutational landscape and significance across 12 major cancer types. Nature 2013;502:333-9. [Crossref] [PubMed]
37. Bailey MH, Tokheim C, Porta-Pardo E, et al. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 2018;173:371-385.e18. [Crossref] [PubMed]
38. Chen C, Liu Y, Rappaport AR, et al. MLL3 is a haploinsufficient 7q tumor suppressor in acute myeloid leukemia. Cancer Cell 2014;25:652-65. [Crossref] [PubMed]
39. Stauffer KM, Elion DL, Cook RS, et al. MLL3 is a de novo cause of endocrine therapy resistance. Cancer Med 2021;10:7692-711. [Crossref] [PubMed]
40. Pensabene M, Von Arx C, De Laurentiis M. Male Breast Cancer: From Molecular Genetics to Clinical Management. Cancers (Basel) 2022;14:2006. [Crossref] [PubMed]
41. Antoniou A, Pharoah PD, Narod S, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet 2003;72:1117-30. [Crossref] [PubMed]
42. Chen S, Parmigiani G. Meta-analysis of BRCA1 and BRCA2 penetrance. J Clin Oncol 2007;25:1329-33. [Crossref] [PubMed]
43. Li H, Wang J, Yi Z, et al. CDK12 inhibition enhances sensitivity of HER2+ breast cancers to HER2-tyrosine kinase inhibitor via suppressing PI3K/AKT. Eur J Cancer 2021;145:92-108. [Crossref] [PubMed]
44. Nunnery SE, Mayer IA. Targeting the PI3K/AKT/mTOR Pathway in Hormone-Positive Breast Cancer. Drugs 2020;80:1685-97. [Crossref] [PubMed]
45. Miricescu D, Totan A, Stanescu-Spinu II, et al. PI3K/AKT/mTOR Signaling Pathway in Breast Cancer: From Molecular Landscape to Clinical Aspects. Int J Mol Sci 2020;22:173. [Crossref] [PubMed]
46. Du L, Li X, Zhen L, et al. Everolimus inhibits breast cancer cell growth through PI3K/AKT/mTOR signaling pathway. Mol Med Rep 2018;17:7163-9. Retracted Publication. [Crossref] [PubMed]
47. Song YN, Geng JS, Liu T, et al. Long CAG repeat sequence and protein expression of androgen receptor considered as prognostic indicators in male breast carcinoma. PLoS One 2012;7:e52271. [Crossref] [PubMed]