Acidosis-associated gene signature defines novel subtypes and dual-target therapeutic candidates in breast cancer

Abstract

Background

Breast cancer remains the most common malignancy in women, and substantial heterogeneity in treatment response and prognosis persists despite multimodal therapies. The acidic tumor microenvironment (TME), driven by metabolic reprogramming and lactate accumulation, is recognized as a key driver of tumor adaptation, immune evasion, and therapeutic resistance. However, the genomic and transcriptomic patterns of acidosis tolerance in human breast cancer, and their implications for subtype stratification and targeted therapy, remain poorly understood.

Methods

We integrated GEO datasets, sgRNA-seq data and breast cancer-associated genes to identified breast cancer acidosis tolerance genes (BCATGs). GO and KEGG enrichment analysis were used to define BCATGs function. We divided breast cancer patients into two subtypes based on BCATGs by consensus clustering. GSEA and GSVA were used to characterized the molecular mechanisms of BCATGs subtypes. CIBERSORT, ESTIMATE, IPS, and TIDE, and oncoPredict were applied to depict the immune microenvironment of BCATGs subtypes. A LASSO-Cox prognostic model was developed and validated, with clinical correlations assessed by Cox regression. Virtual screening against key prognostic genes employed AutoDock Vina, followed by molecular dynamics simulations and MM/PBSA binding energy calculations in GROMACS.

Results

Seventeen BCATGs were identified, predominantly enriched in mitotic regulation and cell cycle, with low mutation rates, predominant copy number gains and notable co-occurrence patterns. Consensus clustering revealed two subtypes: Subtype I and Subtype II. Subtype II exhibited marked activation of proliferative signatures and suppression of differentiation pathways, coupled with a pro-inflammatory yet immunosuppressive immune profile. Subtype II showed greater sensitivity to cell-cycle inhibitors, apoptosis inducers, and proteasome inhibitors. A five-gene LASSO risk model (AURKA, CCNA2, CDC45, EXO1, KIF4A) demonstrated robust prognostic performance, particularly for disease-specific survival (1-year AUC 0.731), with CCNA2 and CDC45 retaining independent prognostic significance after multivariate adjustment. Virtual screening and molecular dynamics identified four lead compounds (SBC-115337, CDK2-IN-4, Bractoppin, Corylin) with stable binding to CCNA2 and CDC45.

Conclusions

This study establishes a novel framework linking acidosis adaptation to breast cancer heterogeneity, identifying BCATGs-driven subtypes with distinct molecular, immunological, and pharmacological profiles. The prognostic model highlights CCNA2 and CDC45 as key drivers of adverse outcomes. These findings provide a foundation for patient stratification, risk assessment, and targeted therapy in breast cancer.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12967-026-08365-x.

Background

Breast cancer is the most common malignancy in women worldwide, with approximately 2.3 million new cases and 685,000 deaths annually [1]. Although substantial progress in multimodal therapy—including surgery, radiotherapy, chemotherapy, endocrine treatment, targeted agents, and immunotherapy [2–7]—has markedly improved survival for many patients, a substantial proportion still face early recurrence, drug resistance, and poor prognosis [8, 9]. These are closely linked to the intrinsic heterogeneity of the disease and the complex regulation within the tumor microenvironment (TME) [10]. Consequently, exploring the biological regulatory patterns associated with TME and leveraging them to refine patient subtyping holds critical importance for advancing precision medicine in breast cancer.

The TME not only serves as a supportive niche for tumor cells but also actively shapes their adaptive behaviors and immune escape capabilities through metabolic reprogramming and acidic stress [11]. The acidic extracellular environment is a hallmark of solid tumors, primarily resulting from rapid proliferation-induced hypoxia, enhanced aerobic glycolysis (Warburg effect), and excessive lactic acid secretion [12]. Experimental studies have shown that dysadherin actively shapes the acidic tumor microenvironment by activating the carbonic anhydrase 9 (CA9) axis, significantly enhancing the proliferation, migration, and acid adaptability of colorectal cancer cells [13]. Acidosis leads to high expression of PD-L1 in cancer cells by enhancing the IFN-γ signaling pathway, resulting in tumor immune escape [14]. In breast cancer, recent experimental studies have shown that acidosis regulates adipocyte G0S2 expression to facilitate adipocyte-tumor cell crosstalk and promote tumor advancement [15]; activates ferroptosis pathways via the ZFAND5/SLC3A2 axis while inducing M1 macrophage polarization [16]; and enables therapeutic sensitization and metastasis suppression when tumor pH is neutralized [17]. These findings collectively demonstrate that acidosis exerts a profound influence on breast cancer progression, invasion, metastasis, and treatment resistance. Establishing a clinically relevant framework to assess and stratify patients according to their acidosis-associated states could therefore provide a more precise basis for diagnosis, prognosis, and individualized therapeutic strategies.

Current studies have predominantly focused on mechanistic insights in cell lines or animal models, the adaptive genomic and transcriptomic patterns underlying acidosis tolerance in human breast cancer remain incompletely understood. The present study systematically identifying breast cancer acidosis tolerance genes (BCATGs) through integrative bioinformatics analysis of tumor acidosis genes and breast cancer-associated genes. We construct novel subtypes based on these genes, characterize their molecular mechanisms, immune features, and drug sensitivities, develop a prognostic risk model, and perform virtual screening for inhibitors against key prognostic genes. These findings offer promising data for patient stratification, prognostic evaluation, and personalized treatment in breast cancer.

Materials and methods

Acquisition of tumor acidosis genes

We systematically retrieved acidosis-related transcriptomic datasets from the Gene Expression Omnibus (GEO) database, including GSE152345 [18], GSE29406 [19], GSE200546 [20], GSE19123 [21], GSE215085, GSE116035 [22], GSE315655 [23], GSE300765 [24], GSE300768 [24], and GSE9649 [25]. Differential expression analysis was conducted using the R package DESeq2 [26] for RNA-seq datasets and R package limma [27] for microarray datasets, with thresholds of |logFC| ≥ 2 and p-value < 0.05 to identify human tumor acidosis genes. Based on sgRNA-seq data from a previous acidosis adaptation model in mouse cell lines [28], we obtained sgRNA abundance data across five acidosis conditions, including low glucose, low nonessential amino acids (NEAA), low glucose with low amino acids, hypoxia, lactic acid accumulation. The MAGeCK [29] was used to screen the genes by using the following thresholds based on the reference paper’s method: neg.fdr < 0.05, -3 < neg.lfc ≤ -2, neg.lfc ≥ 2, and neg.goodsgrna ≥ 3. Subsequently, mouse genes were mapped to human orthologs using the R package biomaRt [30, 31]. UniProt database (https://www.uniprot.org/) used to retrieve genes that cannot be obtained automatically. The genes were defined as mouse tumor acidosis genes (human ortholog versions). Genes at the intersection of human tumor acidosis genes and mouse tumor acidosis genes were defined as tumor acidosis genes.

Acquisition of breast cancer datasets and identification of cancer-associated genes

We retrieved TPM expression data for the TCGA-BRCA dataset from the TCGA database (https://portal.gdc.cancer.gov/) using the R easyTCGA package [32]. Breast cancer samples (labeled “01A”) and normal samples (labeled “11A”) were filtered, yielding 1086 tumor samples and 99 normal samples. Differential expression analysis was conducted using the R package limma [32], with thresholds of |logFC| ≥ 2 and FDR-adjusted p-value < 0.05 to identify breast cancer-associated genes.

For gene expression validation, we integrated GEO datasets GSE42568 [33] and GSE86374 [34] from GEO database. GSE42568 comprised 104 breast cancer samples and 17 normal samples; GSE86374 comprised 124 breast cancer samples and 35 normal samples.The R package SVA [35] used to reduce batch effects, resulting in a merged validation dataset of 228 tumor samples and 52 normal samples. For survival validation, we obtained GEO datasets GSE1456 [36], GSE7390 [37], GSE42568, GSE86166 [38] and GSE96058 [39] from GEO database, and METABRIC dataset from the cBioPortal database (https://www.cbioportal.org/).

Somatic mutation and copy number variation analysis

Somatic mutation data for the TCGA-BRCA dataset were retrieved using the R package easyTCGA [32]. Mutation spectra were visualized using the R package maftools [40]. Additionally, gene mutation information and GISTIC2.0 results for TCGA-BRCA were extracted from the cBioPortal database (https://www.cbioportal.org/), which facilitated copy number variation (CNV) analysis with default thresholds for amplification and deletion calls.

Construction of breast cancer consensus clustering subtypes

TCGA-BRCA samples were cluster with 17 BCATGs by using the R package ConsensusClusterPlus [41]. The clustering algorithm was set to PAM (Partitioning Around Medoids) and the distance metric to Euclidean, with 80% sample resampling and 100% feature resampling over 50 iterations. The maximum number of clusters was set to 10. The optimal number of clusters was determined based on the consensus matrix for k = 2–10. For subtype-specific insights, the R package limma [27] was used to identified differential expression genes between Subtype I and Subtype II, with thresholds of |logFC| ≥ 1 and FDR-adjusted p-value < 0.05 to identify subtype-associated genes.

Functional and pathway enrichment analyses

To investigate the functional roles of the 17 BCATGs, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the R package clusterProfiler [42]. GO terms covered biological processes (BP), cellular components (CC), and molecular functions (MF), while KEGG focused on pathway associations. Gene Set Enrichment Analysis (GSEA) was conducted using clusterProfiler [42], with reference gene sets from GO (BP, CC, MF) and KEGG. The R package GSVA [43] was used for enrichment analysis of Gene Set Variation Analysis (GSVA), with the Hallmark gene set (h.all.v2025.1.Hs.symbols.gmt) from the Molecular Signatures Database [44] (MSigDB; https://www.gsea-msigdb.org/gsea/msigdb) as the reference background.

Immune microenvironment, IPS, and TIDE analyses

To evaluate immune microenvironment differences between BCATGs subtypes, we applied the CIBERSORT algorithm [45] to the TPM expression matrix, using the LM22 signature matrix with 1000 permutations to estimate the relative proportions of 22 immune cell types. Immune and stromal infiltration levels were assessed using the R package ESTIMATE [46]. Immunophenoscore (IPS) data for TCGA-BRCA were retrieved from The Cancer Immunome Atlas (TCIA) database [47] (https://tcia.at/home) to predict responses to CTLA-4 and PD-1 inhibitors across four checkpoint modalities. Tumor immune dysfunction and exclusion (TIDE) scores were computed using the TIDE online tool [48] to infer immune evasion potential.

OncoPredict drug sensitivity analysis

Drug sensitivity analysis was conducted using the R package oncoPredict [49], based on the Genomics of Drug Sensitivity in Cancer 2 (GDSC2) database [50]. IC50 values for 198 anticancer compounds were predicted for TCGA-BRCA samples. Samples were then grouped by BCATGs subtypes, and differential sensitivities between Subtype I and Subtype II were assessed via t-tests. Log2 fold change for IC50 was calculated as log2(Subtype II mean IC50 / Subtype I mean IC50), where log2FC < 0 indicated greater sensitivity in Subtype II. Significant differences were defined as FDR adjust P value < 0.05 and |log2FC| > 0.58. IC50 distributions were visualized using the R package ggplot2 [51]. The effect size was calculated by R packages effectsize [52].

LASSO regression and survival analysis

To select prognostic genes and construct a risk model, we applied LASSO-Cox regression with 10-fold cross-validation to the 17 BCATGs in the TCGA-BRCA dataset using the R package glmnet [53], determining the optimal lambda.1se value. The risk score was calculated based on the LASSO coefficients for the selected genes.

Patients were stratified into high- and low-risk groups using the median risk score. Time-dependent receiver operating characteristic (ROC) curves were generated using the R package timeROC [54] to evaluate the model’s performance for overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Kaplan-Meier survival analysis was analysis by the R package survival [55] to assess the impact of key genes and risk scores on survival outcomes. The c-index of the model was calculated by R package rms [55].

Clinical correlation and prognostic factor analysis

To evaluate the clinical relevance of the risk model, we analyzed associations between risk scores and pathological features in breast cancer patients, including stage, T stage, N stage, M stage, and ER/PR immunohistochemical status. Group differences were compared using t-tests, with p < 0.05 considered significant. A Sankey diagram was generated to visualize the relationships among risk groups, clinical stages, and molecular subtypes using the R package ggalluvial [57]. Univariate and multivariate Cox regressions were analysis based on key genes and clinical features using the cph function from the R package survival [54]; factors with p < 0.05 in univariate analysis were included in the multivariate model to identify independent prognostic predictors, with hazard ratios (HRs) and 95% confidence intervals calculated. Forest plots for both univariate and multivariate results were generated using the R package forestplot [58]. Following this, the multivariate model was recalculated using the cph function from the R package rms [56], and a nomogram was constructed with the nomogram function for predictive visualization.

Molecular docking with Autodock Vina

From the Tao Shu database, we retrieved 1441 drugs associated with Cell Cycle/Checkpoint, JAK/STAT signaling, and PI3K/Akt/mTOR signaling pathways. Drug SDF structures were converted to 3D using OpenBabel [59] (version 3.1.1). Protein PDB structures for CCNA2 and CDC45 were obtained from the AlphaFold database (https://alphafold.ebi.ac.uk/). Ligands and proteins were preprocessed using AutoDock Tools (https://ccsb.scripps.edu/mgltools/), defining pocket parameters based on the protein’s spatial structure. AutoDock Vina [60, 61] (version 1.2.5) was applied to molecular docking, with parameters set to 10,000 seeds and exhaustiveness of 4. Docking results were visualized and analyzed using PyMOL [62] (version 3.1.6).

Molecular dynamics simulation with GROMACS and MM/PBSA binding energy analysis

Molecular dynamics simulations were conducted using GROMACS (version 2023.4) [63] to assess the atomic-level stability of the top candidate compounds with CCNA2 and CDC45. Drug topology parameters (.gro, .top, .itp) were built using sobtop (Tian Lu, Sobtop, http://sobereva.com/soft/Sobtop). Protein topology was generated with the AMBER99SB-ILDN force field, and complexes were assembled with the drugs. Systems were solvated in a TIP3P water box, neutralized with counterions, and energy-minimized using steepest descent followed by conjugate gradient method. NVT equilibration and NPT production simulations applied to stabilize the systems at 310 K and 1 atm.

Structural stability was evaluated by calculating root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and radius of gyration (Rg). Protein-ligand interactions were further analyzed through hydrogen bond counts, and solvent-accessible surface area (SASA) over the simulation trajectory. Results were visualized using Python package matplotlib [64].

Binding free energies of the drug-protein complexes were computed using the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method [65]. Calculations were based on snapshots from the last 30 ns (70–100 ns) of stable trajectories. The binding energy formula is:

where represents vacuum binding energy (typically negative, favoring binding) and quantifies solvation effects (typically positive, opposing binding); lower indicates greater complex stability.

Statistical analysis

All data processing and analysis in this article were conducted utilizing R software version 4.5.0. The Wilcoxon Rank Sum Test was used for comparison between the two groups. If not specified, the correlation coefficient between different molecules was calculated by Spearman correlation analysis, and a p-value of less than 0.05 was used as the criterion for significant difference.

Results

Identification of acidosis-adaptive genes linked to breast cancer pathogenesis

By integrating human tumor acidosis genes and mouse tumor acidosis genes, we identified 471 tumor acidosis adaptive genes (Supplementary Table S1). Differential analysis of TCGA-BRCA revealed 552 breast cancer-associated genes (149 upregulated, 403 downregulated) (Fig. 1A). These genes effectively discriminated tumor from normal tissues. Intersecting them with acidosis-adaptive genes defined 17 BCATGs: AURKA, AURKB, BUB1B, CCNA2, CDC45, CENPA, CENPM, DTL, EXO1, GTSE1, KIF18B, KIF4A, NDC80, NUF2, SPC24, TTK, and UHRF1 (Fig. 1B). Boxplots confirmed significantly higher BCATGs expression in tumors within TCGA-BRCA (Fig. 1C) and GEO datasets (Fig. 1D). Chromosomal mapping localized these genes predominantly to chromosomes 1 (DTL, EXO1, NUF2), 17 (AURKB, KIF18B), 19 (SPC24, UHRF1), and 22 (CDC45, CENPM, GTSE1), implying potential co-regulatory mechanisms in genomic hotspots (Fig. 1E).

Fig. 1.

Screening and validation of BCATGs. A The heatmap of breast cancer-associated genes in TCGA-BRCA. B The Venn diagram of intersection of tumor acidosis genes and breast cancer-associated genes. C-D The expression boxplot of BCATGs in TCGA-BRCA and GEO cohort. E The BCATGs location annotation in chromosome. *** indicates P-value < 0.001, ** indicates P-value < 0.01, * indicates P-value < 0.05

GO and KEGG enrichment analysis of BCATGs

GO and KEGG enrichment analysis were applied to elucidate the functional roles of the 17 BCATGs in breast cancer (Fig. 2I, Supplementary Table S2). In biological processes (BP), enrichments centered on mitotic regulation, including chromosome segregation, mitotic nuclear division, cell cycle checkpoint signaling, mitotic spindle organization, and regulation of mitotic cell cycle (Fig. 2E). Cellular components (CC) highlighted microtubule-related structures, such as microtubules, microtubule-associated complexes, mitotic spindles, midbodies, and cytoplasmic microtubules (Fig. 2F). Molecular functions (MF) were linked to kinase activities of key genes like AURKA, BUB1B, and TTK, encompassing histone H3 kinase activity, histone kinase activity, eukaryotic translation initiation factor 2 alpha kinase activity, ribosomal protein S6 kinase activity, and DNA-dependent protein kinase activity (Fig. 2G). KEGG pathways implicated cell proliferation, genomic stability, immune/inflammatory responses, and metabolic regulation, notably cell cycle, progesterone-mediated oocyte maturation, mismatch repair, p53 signaling, and oocyte meiosis (Fig. 2H). Pathway similarity network analysis showed strong overlaps among BP and MF GO terms (Fig. 2A and Fig. 2C), moderate among CC terms (Fig. 2B), but weaker among KEGG pathways (Fig. 2D).

Fig. 2.

The GO and KEGG analysis of BCATGs. A-D Gene driven pathway similarity network of BP, CC, MF and KEGG. E-H The sankey diagram of gene and pathways. I The bar plot of BP, CC, MF and KEGG

Somatic mutation and copy number variation analysis of BCATGs

To characterize somatic mutations in the 17 BCATGs, we analyzed TCGA-BRCA breast cancer samples. Missense mutations predominated, accompanied by single nucleotide polymorphisms (SNPs) and deletions (DELs) (Fig. 3A). C > T transitions were the most frequent single nucleotide variants (SNVs), followed by C > G and C > A, as illustrated in the stacked bar plot of mutation profiles (Fig. 3B). Among the BCATGs, 14 harbored somatic mutations, affecting 63 of 991 mutated samples (6.36% overall). KIF4A exhibited the highest mutation rate (2%), while CENPM, SPC24, and CENPA showed no mutations (Fig. 3C). The mutation co-occurrence heatmap (Fig. 3D) illustrated associations among the 17 BCATGs, revealing 12 statistically significant gene pairs (p < 0.05), with UHRF1-EXO1 and AURKA-KIF4A pairs being particularly notable (p < 0.01). Kaplan-Meier survival analysis indicated poorer overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in samples with BCATGs mutations, though differences lacked statistical significance (Fig. 3E-H). GISTIC2.0 analysis detected CNVs in 16 BCATGs, with CENPA unassessable due to NA results. Predominantly, gains were observed among the evaluable genes, implying amplification-driven overexpression in breast cancer (Fig. 3I).

Fig. 3.

The mutation analysis of BCATGs in TCGA-BRCA cohort. A The mutation summary in TCGA-BRCA. B The boxplot and stacked bar plot of mutation types in TCGA-BRCA. C The mutation oncoplot of BCATGs in TCGA-BRCA. D The gene mutation correlation heatmap of BCATGs in TCGA-BRCA. E-H The OS, DSS, DFI and PFI survival KM curve of mutation of BCATGs in TCGA-BRCA. I The lollipop chart of the CNV of BCATGs in TCGA-BRCA

BCATGs subtype consensus clustering and validation

To explore the significance of BCATGs in the clinical stratification of breast cancer patients, we conducted consensus clustering on TCGA-BRCA samples using the 17 BCATGs. Cumulative distribution function (CDF) plots, delta area plots and heatmaps (Fig. 4A-E) demonstrated that both k = 2 and k = 3 exhibited relatively stable and flat curves, indicating good clustering stability. Principal component analysis (PCA) showed that at k = 2 (Fig. 4F), the two clusters were distinctly separated into two modules in the spatial distribution, with partial overlap in the 95% confidence intervals. In contrast, at k = 3 (Fig. 4G), the 95% confidence interval of Cluster 1 was extensively overlapped by those of Cluster 2 and Cluster 3. Comprehensive evaluation of these results indicated that when k = 3, Cluster 1 likely represented a transitional state between Cluster 2 and Cluster 3, potentially lacking independent biological characteristics. In contrast, k = 2 clearly partitioned samples into two distinct biological states (Cluster 1 and Cluster 2), where differentially expressed genes between the two groups were more likely to represent key drivers of disease progression. Therefore, k = 2 was selected as the optimal clustering number, dividing breast cancer samples into Subtype I (Cluster1, 513 cases) and Subtype II (Cluster2, 573 cases). Subtype I was enriched in Luminal A (82.26%), while Subtype II predominantly comprised TNBC (31.06%), HER2+ (11.17%), and Luminal B (31.41%) subtypes (total 73.64%; p < 0.05) (Fig. 4D). Boxplots revealed significantly higher expression of all 17 BCATGs in Subtype II (p < 0.001; Fig. 4E).

Fig. 4.

Consensus clustering analysis of BCATGs. A-B The cumulative distribution function plot of consensus clustering results. C-E The heatmap of consensus clustering result for TCGA-BRCA in k = 2, k = 3 and k = 4. F-G The PCA plot of clustering result for k = 2 and k = 3. H The stacked bar plot of BCATGs subtypes and PAM50 subtype. I The expression boxplot of BCATGs of BCATGs subtypes in TCGA-BRCA. J-M The OS, DSS, DFI and PFI survival KM curve of mutation of BCATGs subtypes in TCGA-BRCA. *** indicates P-value < 0.001, ** indicates P-value < 0.01, * indicates P-value < 0.05

To further elucidate the relationship between the BCATGs signature and established PAM50 intrinsic subtypes, we compared the expression levels of the 17 BCATGs across PAM50 subtypes. All 17 genes exhibited significantly lower expression in Luminal A tumors compared with Luminal B, HER2+, and TNBC subtypes (Fig. S1A). Moreover, within each PAM50 subtype, the 17 BCATGs were consistently and significantly upregulated in BCATG Subtype II relative to Subtype I (Fig. S1B-E). Kaplan-Meier survival curves demonstrated inferior DSS, DFI, and PFI in Subtype II compared to Subtype I, with statistical significance (Fig. 4F-I). To further evaluate whether BCATGs subtypes provide additional prognostic information for PAM50 subtype we checked subgroup survival within each PAM50 subtype using the same BCATGs subtype (Fig. S2). Within the clinically low-risk Luminal A subtype, patients classified as BCATGs Subtype II exhibited significantly worse DFI (HR = 2.22, 95% CI 1.11–4.46, p = 0.025) (Fig. S2C) and PFI (HR = 1.71, 95% CI 1.02–2.89, p = 0.044) (Fig. S2D). In contrast, no statistically significant survival differences were observed between BCATGs subtypes within the more aggressive Luminal B, HER2 + or TNBC subtype.

To evaluate the independent prognostic contribution of BCATGs subtypes between PAM50 subtypes and clinical variables, we used multivariate Cox regression analysis, with the clinical characteristics included: BCATGs subtype, PAM50 subtype, clinical stage, ER status and PR status (Fig. S3). After adjustment, subtype II retained independent prognostic factors in DSS (HR = 2.02, P = 0.013) and PFI (HR = 1.59, P = 0.036), but had no statistical significance in OS (HR = 1.26, P = 0.27) and DFI (HR = 1.40, P = 0.288). The PAM50 subtype was not statistically significant in all survival states. ER positive status has HR > 1 in all survival types, but is only statistically significant in DSS, DFI, and PFI. PR positive status has protective significance and statistical significance in all survival states (HR < 1, P < 0.05).

GSEA and GSVA enrichment analyses of BCATGs subtypes

To uncover molecular differences between BCATGs subtypes, 333 differentially expressed genes (191 upregulated and 142 downregulated in Subtype II) were identified between Subtype I (513 samples) and Subtype II (573 samples) (Fig. 5A). GSEA based on these genes revealed pronounced enrichments in Subtype II (Supplementary Table S3). Biological processes emphasized cell division and chromosome dynamics, such as cell division (NES = 4.39), nuclear chromosome segregation (NES = 4.51), and chromosome segregation (NES = 4.45) (Fig. 5B). Cellular components focused on structural elements like membraneless organelles (NES = 4.59), nuclear lumen (NES = 4.29), and mitotic spindle (NES = 3.34) (Fig. 5C). Molecular functions highlighted hydrolase and binding activities, including hydrolase activity acting on acid anhydrides (NES = 2.52), chromatin binding (NES = 1.79), and oxidoreductase activity (NES = 1.91) (Fig. 5D). KEGG pathways centered on cell cycle and mitosis-related processes, such as cell cycle (NES = 3.34), motor proteins (NES = 2.44), cellular senescence (NES = 2.35), and oocyte meiosis (NES = 2.21), with no negative enrichments (Fig. 5E).

Fig. 5.

GSEA and GSVA for TCGA-BRCA cohort A The heatmap of DEGs between Subtype I and Subtype II. B-E The dot plot of GSEA enrichment analysis results of BP, CC, MF and KEGG. F The heatmap of enrichment scores for GSVA

Further GSVA analysis using the h.all.v2025.1.Hs.symbols gene set identified 24 significantly enriched Hallmark pathways (Fig. 5F, Supplementary Table S4), with 18 upregulated and 6 downregulated in Subtype II. Upregulated pathways aligned with high proliferation, dedifferentiation, and mitosis-driven malignancy, including E2F TARGETS, G2M CHECKPOINT, MYC TARGETS V1, MYC TARGETS V2, MTORC1 SIGNALING, and MITOTIC SPINDLE. Downregulated pathways reflected loss of homeostasis, suppressed differentiation, and epithelial collapse, such as APICAL JUNCTION, ESTROGEN RESPONSE EARLY, and TGF BETA SIGNALING.

Immune microenvironment characterization in BCATGs subtypes

To assess immune microenvironment differences between BCATGs subtypes, we applied CIBERSORT to estimate abundances of 22 immune cell types. Among these, 12 showed significant differences (p < 0.05): activated CD4 memory T cells, follicular helper T cells, M0 macrophages, M1 macrophages, and resting NK cells were elevated in Subtype II, while memory B cells, resting CD4 memory T cells, monocytes, resting dendritic cells, resting mast cells, activated NK cells, and neutrophils were reduced (Fig. 6A). Immune cell correlation heatmap identified 39 correlations between 12 significant differences immune cells (FDR-adjust P value<0.05, 19 positive correlations and 20 negative correlations) (Fig. 6B). The top positive correlations were Macrophages M1 and T cells CD4 memory activated (cor = 0.356), and the top negative correlations were NK cells resting and NK cells activated (cor=-0.638). The gene-immune cell correlation heatmap revealed 93 significant pairs (FDR-adjust P value<0.05; 34 positive, 59 negative) (Fig. 6C). The top positive associations were CENPA and T cells CD4 memory activated (cor = 0.348), and the top negative associations were CENPA and Mast cells resting (cor=-0.343). ESTIMATE analysis showed significantly lower ESTIMATE, Immune, and Stromal Scores in Subtype II, alongside higher Tumor Purity (all p < 0.05) (Fig. 6D-G). IPS scores from TCIA were lower in Subtype II across four checkpoint modalities (CTLA4(-)PD1(-), CTLA4(-)PD1(+), CTLA4(+)PD1(-), CTLA4(+)PD1(+)), with statistical significance in CTLA4(-)PD1(-) and CTLA4(+)PD1(-) (p < 0.05) (Fig. 6H-K). TIDE scores were markedly higher in Subtype II (p < 0.05) (Fig. 6L), indicating greater immune evasion potential.

Fig. 6.

Analysis of immune microenvironment characteristics. A The boxplot and heatmap of the comparison of immune cell abundance between subtype I and subtype II. The red font represents the elevation of immune cells in subtype II. The blue font represents the elevation of immune cells in subtype (I) B The heatmap of correlation between significant differences immune cells. C The heatmap of correlation between BCATGs and significant differences immune cells. D-G The boxplot of ESTIMATE score, immune score, stromal score and tumor purity between subtype I and subtype (II) H-K The boxplot of IPS score CTLA4 (-) PDL1 (-), CTLA4 (-) PDL1 (+), CTLA4 (+) PDL1 (-), and CTLA4 (+) PDL1 (+) between subtype I and subtype II. L The boxplot of TIDE score between subtype I and subtype II. *** indicates P-value < 0.001, ** indicates P-value < 0.01, * indicates P-value < 0.05

Drug sensitivity analysis in BCATGs subtypes

To evaluate differential anticancer drug sensitivities between BCATGs subtypes, we applied oncoPredict to the TCGA-BRCA dataset, screening 198 compounds and identifying 20 compounds with different sensitivity (FDR-adjust P value < 0.05 and |log2FC| > 0.58, supplementary Table S5). Within 20 sensitivity compounds, 17 were sensitive in Subtype II and 3 were sensitive in Subtype I. 17 Subtype II sensitive compounds (Fig. 7A-H and Fig. S4A-I) encompassing cell cycle regulators (MK-1775, MK-8776, AZD6738), proteasome inhibitor (Bortezomib), apoptosis inducers (ABT737, Venetoclax, WEHI-539), kinase inhibitors (AZD7762, Foretinib, YK-4-279, Docetaxel), and others such as autophagy inhibitor (ULK1_4989), DNA binder (Dactinomycin), NEDD8 activator inhibitor (Pevonedistat), telomerase inhibitor (Telomerase Inhibitor IX), CDK9 inhibitor (Sepantronium bromide), and p53 activator (UMI-77). 3 Subtype I sensitive compounds (Fig. 7I and Fig S4J-K) including CDK1 inhibitor (RO-3306), TGF-β receptor inhibitor (SB505124), and Wnt pathway inhibitor (BMS-754807). The absolute value of effect size of 20 drugs ranged from 0.17 to 1.35. Within 20 durgs, 19 drugs had small effects or above, of which 6 drugs had large effects (5 Subtype II sensitive, 1 Subtype I sensitive), 6 drugs had medium effects (4 Subtype II sensitive, 5 Subtype I sensitive), and 7 drugs had small effects (7 Subtype II sensitive).

Fig. 7.

Anti-tumor drugs sensitivity analysis. A-H The boxplot of subtype II sensitive drugs: ULK1_4989 (A), Dactinomycin (B), Pevonedistat (C), AZD7762 (D), Bortezomib (E), MK-1775 (F), Foretinib (G), YK-4-279 (H). I The boxplot of subtype I sensitive drugs: BMS-754,807

LASSO regression for prognostic gene selection and validation

To develop a prognostic risk model based on the 17 BCATGs, we applied LASSO-Cox regression with 10-fold cross-validation (Fig. 8A-B). Five lasso feature genes were ultimately selected as the optimal signature, yielding the following risk score formula:

Fig. 8.

LASSO-Cox analysis of BCATGs. A-B The prognostic risk model (A) and variable trajectories (B) of LASSO-Cox regression model. C The stacked bar plot of LASSO risk group and BCATGs subtype. D The expression box plot of five lasso feature genes between high and low risk group in TCGA-BRCA. E-H The OS, DSS, DFI and PFI survival KM curve of risk group in TCGA-BRCA. I-L The 1-year, 3-year and 5-year time-dependent ROC curves of risk score of OS, DSS, DFI and PFI. M-Q The DSS survival KM curve of AURKA, CCNA2, CDC45, EXO1 and KIF4A in TCGA-BRCA

Patients in the TCGA-BRCA cohort were stratified into high- and low-risk groups according to the median risk score derived from the training set. Association analysis revealed a strong concordance between risk groups and BCATGs subtypes: 98.2% of high-risk patients belonged to Subtype II, while 93.2% of low-risk patients belonged to Subtype I (Fig. 8C). Expression validation demonstrated that all five signature genes were significantly upregulated in the high-risk group compared with the low-risk group in the TCGA-BRCA cohort (Fig. 8D). This differential expression pattern was consistently reproduced in five independent GEO validation cohorts and the METABRIC dataset (Fig. S5A-F), confirming the robustness of the signature across external populations. Kaplan-Meier survival analysis showed that patients in the high-risk group had significantly poorer overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) than those in the low-risk group in the TCGA cohort, with a similar trend observed for disease-free interval (DFI) that did not reach statistical significance (Fig. 8E-H). These prognostic differences were independently validated in the five GEO cohorts and the METABRIC dataset, where high-risk patients consistently exhibited significantly inferior OS across all external cohorts (Fig. S5G-L). Time-dependent receiver operating characteristic (ROC) analysis further evaluated the model’s discriminative performance. In TCGA cohort, DSS perform the best predictive accuracy (1-year AUC = 0.731, 3-year AUC = 0.627, 5-year AUC = 0.583), and moderate for OS (1-year AUC = 0.637, 3-year AUC = 0.598, 5-year AUC = 0.567), and variable for DFI and PF (Fig. 8I-L). In validation sets (Fig. S5M-R), GSE96058 (AUC = 0.687) and METABRIC (AUC = 0.628) showed best predictive accuracy in 1-year OS, but worse in GSE1456 (AUC = 0.417) and GSE7390 (AUC = 0.391). Despite initial variability at the 1-year timepoint, most validation cohorts demonstrated improved and stabilized predictive performance at 3-year and 5-year horizons. The GSE86166 showed the highest AUC in 3-year OS prediction (AUC = 0.711), and 5 of 6 validation sets had the AUC higher than 0.6 in 3-year OS prediction. All validation sets had the AUC higher than 0.6 in 5-year OS prediction. In addition, Harrell’s concordance index (C-index) was calculated for TCGA-BRCA dataset and validation datasets (Supplementary Table S6). In the TCGA training cohort, the model demonstrated moderate predictive accuracy for Overall Survival (OS) with a C-index of 0.574. Notably, the predictive performance was significantly higher for Disease-Specific Survival (DSS), reaching a C-index of 0.621, which aligns with the superior AUC observed in the ROC analysis for this endpoint. In the independent external validation sets, the model showed consistent prognostic value. The validation cohorts GSE1456 and GSE86166 yielded the highest C-index values of 0.664 and 0.650, respectively. Furthermore, the model maintained stable performance across the remaining validation datasets, with C-index values ranging from 0.602 to 0.616 in GSE7390, GSE42568, and GSE96058. The METABRIC cohort also confirmed the robustness of the signature with a C-index of 0.5915. To further elucidate the relationship between the lasso risk group and established PAM50 intrinsic subtypes, used the same lasso risk group to subgroup survival analysis within each PAM50 subtype (Fig. S6). Within the clinically low-risk Luminal A subtype, patients classified as BCATGs Subtype II exhibited significantly worse DFI (HR = 2.4, 95% CI 1.18–4.85, p = 0.015) (Fig. S6C) and PFI (HR = 1.75, 95% CI 1.02–2.99, p = 0.041) (Fig. S6D). In contrast, no statistically significant survival differences were observed between BCATGs subtypes within the more aggressive Luminal B, HER2 + or TNBC subtype. Given the superior predictive performance of the model for DSS, we further examined the prognostic impact of the five individual genes on disease-specific survival. Univariate Cox regression revealed that high expression of AURKA, CCNA2, CDC45, and EXO1 was significantly associated with worse DSS, whereas KIF4A showed a similar trend that did not reach statistical significance (Fig. 8M-Q).

Clinical correlations and prognostic factors associated with risk scores

To assess the clinical relevance of the risk model, we examined associations between risk scores and pathological features in breast cancer patients, including stage, T stage, N stage, M stage, and ER/PR immunohistochemical status. Significant differences were observed for stage, T stage, N stage, and ER/PR (p < 0.05), but not M stage (Fig. S7). Sankey diagram illustrated the interplay among risk groups, clinical stages, and molecular subtypes (Fig. 9A).

Fig. 9.

Prognostic Model for univariate and multivariate cox regression. A The sankey plot of risk group and clinical pathologic features. B The forest plot of univariate cox regression of five key genes and clinical pathologic features. C-D The forest plot and nomogram of multivariate cox regression

Univariate Cox regression analysis was subsequently used to evaluate the prognostic impact of these clinicopathological variables together with the five signature genes (Fig. 9B). Advanced clinical stage, T stage, and N stage were associated with significantly increased hazard ratios (HR > 1, p < 0.05). PR-positive status was significantly protective (HR < 1, p < 0.05), whereas ER- positive status showed a modest that did not reach statistical significance. All five signature genes (AURKA, CCNA2, CDC45, EXO1, and KIF4A) demonstrated HR values greater than 1 with statistical significance (all p < 0.05), indicating consistent adverse prognostic effects in the univariate setting. Multivariable Cox regression was then conducted (Fig. 9C-D), simultaneously incorporating clinical stage, PR status, and the five signature genes. After adjustment, clinical stage and PR-positive status retained their direction and statistical significance. Among the signature genes, CDC45 and CCNA2 remained independent predictors of poor outcome (HR > 1, p < 0.05), whereas EXO1 showed a non-significant trend toward adverse prognosis (HR > 1, p > 0.05). Notably, AURKA and KIF4A exhibited reversal of their hazard ratios (HR < 1, p < 0.05) after multivariable adjustment. Variance inflation factor (VIF) analysis revealed low collinearity for clinical stage (VIF = 1.31) and PR status (VIF = 1.30), but moderate to high collinearity among the signature genes (VIF range 4.62–6.16), with EXO1 showing the highest value (VIF = 6.16), followed by CCNA2 (VIF = 5.82), KIF4A (VIF = 5.16), and AURKA (VIF = 4.80).

Molecular docking to identify potential leading drugs

Building on the prognostic importance of CCNA2 and CDC45, we conducted molecular docking with 1441 drugs from the Tao Shu database. Ridge plots demonstrated robust binding affinities, averaging − 6.572 kcal/mol for CCNA2 and − 7.009 kcal/mol for CDC45 (Fig. 10A), with pathway-specific means of -6.702 kcal/mol for Cell Cycle/Checkpoint, -6.897 kcal/mol for JAK/STAT signaling, and − 6.897 kcal/mol for PI3K/Akt/mTOR signaling (Fig. 10B). Dockings with affinity < -8 kcal/mol were deemed high-reliability, identifying 56 drugs with such affinities to both targets. Among these, 24 drugs simultaneously formed hydrogen bonds with CCNA2 and CDC45, representing the most promising candidates (Fig. 10C; Table 1), averaging − 8.341 kcal/mol for CCNA2 (range − 9.184 kcal/mol to -8.004 kcal/mol) and − 8.312 kcal/mol for CDC45 (range − 11.8 kcal/mol to -8.019 kcal/mol) remaining 20 visualized by pathway in Supplementary Figs. S8 for Cell Cycle/Checkpoint, S9 for JAK/STAT, and S10 for PI3K/Akt/mTOR).

Fig. 10.

Virtual screening of dual target drugs. A The energy ridge plot of drugs docking with CCNA2 and CDC45. B The energy ridge plot of drugs docking with Cell Cycle/Checkpoint, JAK/STAT signaling and PI3K/Akt/mTOR signaling pathways. C The docking energy heatmap of 24 drugs with hydrogen bonds simultaneously with CCNA2 and CDC45. D-K Visualization of molecular docking: SBC-115337 with CCNA2 (D) and CDC45 (E), CDK2-IN-4 with CCNA2 (F) and CDC45 (G), Bractoppin with CCNA2 (H) and CDC45 (I), and Corylin with CCNA2 (J) and CDC45 (K)

Table 1.

The docking affinity value of 24 drugs to CCNA2 and CDC45

DrugName	CCNA2_AffinityValue	CDC45_AffinityValue	Average_AffinityValue
SBC-115337	-9.184	-11.8	-10.492
CDK2-IN-4	-8.723	-9.868	-9.2955
Bractoppin	-8.943	-9.105	-9.024
Corylin	-8.033	-9.987	-9.01
DDRI-18	-8.771	-9.221	-8.996
JAK-IN-5	-8.52	-8.966	-8.743
WAY-600	-8.322	-9.109	-8.7155
PTC-028	-8.067	-9.339	-8.703
CX-6258 hydrochloride	-8.135	-9.216	-8.6755
EX229	-8.407	-8.778	-8.5925
HTH-01-015	-8.261	-8.891	-8.576
AMG 925	-8.756	-8.087	-8.4215
IPR-803	-8.363	-8.427	-8.395
MK8722	-8.216	-8.557	-8.3865
Unesbulin	-8.272	-8.475	-8.3735
Belumosudil	-8.197	-8.534	-8.3655
Palbociclib	-8.298	-8.22	-8.259
USP28-IN-3	-8.168	-8.319	-8.2435
Tucatinib	-8.045	-8.402	-8.2235
Withanolide A	-8.268	-8.019	-8.1435
mTOR inhibitor 9f	-8.004	-8.275	-8.1395
XMD8-92	-8.1	-8.153	-8.1265
Torin 2	-8.099	-8.068	-8.0835
Risdiplam	-8.02	-8.134	-8.077

The top binders to CCNA2, ranked by lowest binding energy, were SBC-115,337 (-9.184 kcal/mol), Bractoppin (-8.943 kcal/mol), DDRI-18 (-8.771 kcal/mol), AMG 925 (-8.756 kcal/mol), and CDK2-IN-4 (-8.723 kcal/mol). For CDC45, the top binders were SBC-115,337 (-11.8 kcal/mol), Corylin (-9.987 kcal/mol), CDK2-IN-4 (-9.868 kcal/mol), PTC-028 (-9.339 kcal/mol), and DDRI-18 (-9.221 kcal/mol). Subsequently, we ranked the 24 drugs by their average binding affinities to both targets (from lowest to highest energy, indicating increasing importance), prioritizing those below − 9 kcal/mol to identify four promising candidates: SBC-115,337 (Fig. 10D-E), CDK2-IN-4 (Fig. 10F-G), Bractoppin (Fig. 10H-I), and Corylin (Fig. 10J-K). The remaining drugs are visualized in supplementary figures S8-10.

Molecular dynamics simulation and MM/PBSA analysis of potential dual-target drugs

100 ns molecular dynamics simulations and MM/PBSA analysis were used to evaluate the atomic-level stability of the four top candidates (SBC-115337, CDK2-IN-4, Bractoppin, and Corylin) with CCNA2 and CDC45 (Table 2). For CCNA2 complexes, systems equilibrated after 20 ns (Fig. 11A-B). CDK2-IN-4, Bractoppin, and Corylin showed stable RMSDs (0.59 nm, 0.75 nm, 0.34 nm), while SBC-115,337 exhibited higher dynamics (2.03 nm), suggesting conformational flexibility. Rg and SASA analyses confirmed stability post-40 ns, with most Rg values at ~ 2.28 nm (SBC-115337 at 2.52 nm, indicating potential expansion) and SASA at ~ 240 nm² (Fig. 11C-F). RMSF highlighted fluctuations in residues 1-200 as potential active sites and stability in 201–432 as structural domains (Fig. 11G). Hydrogen bond occupancy revealed CDK2-IN-4 with the highest density (2–3 bonds predominant), followed by SBC-115,337 and Bractoppin (1–2/2–3), and Corylin with minimal (mostly 0) (Fig. 11H-L).

Table 2.

MM/PBSA binding-free-energy for CCNA2 and CDC45 system

Complex	Δevdw (kJ/mol)	ΔEele (kJ/mol)	ΔGsolv (kJ/mol)	ΔGgas (kJ/mol)	ΔGtotal (kJ/mol)
CCNA2-SBC-115337	-208.278 ± 13.61	-29.476 ± 8.25	104.556 ± 20.07	-237.754 ± 15.61	-133.197 ± 11.48
CCNA2-CDK2-IN-4	-266.751 ± 8.35	-49.089 ± 8.07	155.012 ± 10.66	-315.840 ± 11.26	-160.828 ± 12.01
CCNA2-Bractoppin	-214.905 ± 13.81	-48.908 ± 21.22	137.234 ± 21.57	-263.813 ± 22.94	-126.579 ± 13.96
CCNA2-Corylin	-178.741 ± 11.74	-18.544 ± 7.35	84.361 ± 12.66	-197.285 ± 16.49	-112.924 ± 10.65
CDC45-SBC-115337	-223.107 ± 9.05	-34.947 ± 10.00	121.707 ± 23.31	-258.054 ± 11.58	-136.346 ± 18.08
CDC45-CDK2-IN-4	-150.028 ± 8.91	-37.580 ± 13.25	97.225 ± 21.71	-187.608 ± 15.24	-90.383 ± 15.79
CDC45-Bractoppin	-203.786 ± 9.21	-65.004 ± 17.02	145.548 ± 28.72	-268.790 ± 22.63	-123.242 ± 17.31
CDC45-Corylin	-173.448 ± 6.44	-30.095 ± 7.26	100.113 ± 9.41	-203.543 ± 8.03	-103.431 ± 12.74

Fig. 11.

Molecular dynamics simulation and MM/PBSA score validation of top 4 candidate drugs. A-L Molecular Dynamics Simulation of CCNA2 System: RMSD curve (A) and box plot of the final 10ns RMSD value (B), Rg curve (C) and box plot of the final 10ns Rg value (D), SASA curve (E) and box plot of the final 10ns SASA value (F), RMSF curve (G), hydrogen bond quantity density ridge plot (H), hydrogen bond quantity curve (I-L). M-X Molecular Dynamics Simulation of CDC45 System: RMSD curve (M) and box plot of the final 10ns RMSD value (N), Rg curve (O) and box plot of the final 10ns Rg value (P), SASA curve (Q) and box plot of the final 10ns SASA value (R), RMSF curve (S), hydrogen bond quantity density ridge plot (T), hydrogen bond quantity curve (U-X)

For CDC45 complexes, equilibration occurred after 20 ns (Fig. 11M-N), contrasting with CCNA2’s patterns. SBC-115,337 and Bractoppin formed the most stable structures (RMSD 0.22 nm and 0.28 nm), followed by Corylin (0.43 nm) and CDK2-IN-4 (0.69 nm). Rg values ranged from 2.62 to 2.72 nm, with SASA stabilizing at ~ 270 nm² (Bractoppin slightly higher at 277 nm²) (Fig. 11O-R). RMSF indicated a peak in residues 100–200 as a potential active region (Fig. 11S). Hydrogen bond analysis showed CDK2-IN-4 with the highest occupancy (1–2 bonds predominant), SBC-115,337 and Bractoppin moderate (0–1), and Corylin minimal (mostly 0) (Fig. 11T-X).

MM/PBSA calculations from the last 30 ns revealed CDK2-IN-4 with the best affinity to CCNA2 (-160.82 kJ/mol), followed by SBC-115,337 (-133.20 kJ/mol), Bractoppin (-126.58 kJ/mol), and Corylin (-112.92 kJ/mol). For CDC45, SBC-115,337 excelled (-136.35 kJ/mol), ahead of Bractoppin (-123.24 kJ/mol), Corylin (-103.43 kJ/mol), and CDK2-IN-4 (-90.38 kJ/mol).

Discussion

The tumor microenvironment in breast cancer is now widely regarded as a central driver of disease progression and treatment response. Metabolic dysregulation within the TME, particularly chronic extracellular acidosis, has emerged as one of its most biologically active hallmarks [66]. Rapid tumor cell proliferation, enhanced Warburg effect, and excessive lactate secretion create a local hypoxic and acidic milieu that profoundly shapes cancer cell behavior [67–69]. Hypoxia stabilizes HIF-1α, promoting polarization of tumor-associated macrophages toward an M2 phenotype and facilitating tumor progression and immune escape [70]. Acidosis suppresses IFN-γ secretion, impairs CD8 + T-cell cytotoxicity, and contributes to immune suppression [71, 72]. Collectively, these observations position TME acidosis as a pivotal factor in tumor advancement, immune evasion, and therapeutic resistance. Given its influence across multiple malignant processes, acidosis tolerance may serve as a valuable axis for patient stratification, prognostic assessment, and personalized treatment design in breast cancer.

In the present study, we identified 17 BCATGs by intersecting tumor acidosis genes with breast cancer-associated differentially expressed genes from TCGA-BRCA. Subsequent functional analyses divulged that these genes are preponderantly engaged in mitotic regulation, microtubule-related structures, kinase activities, cell proliferation, genomic stability, immune/inflammatory responses, and metabolic regulation. The acidic environment can induce the accumulation of tetraploid cells and the activation of DNA damage response, strengthening the mitotic checkpoint and repair pathways in tumor cells [73]. The imbalance in the expression of acid-base transporters in breast cancer is significantly correlated with pH homeostasis, lymph node metastasis, and poor prognosis [74]. The BCATGs are highly expressed in breast cancer samples, suggesting that breast cancer cells may enhance the above mechanisms to counteract the damage caused by the acidic microenvironment. The analysis of somatic mutations and copy number variations of the 17 BCATGs showed that the overall mutation rate was relatively low (6.36%), and showed no statistical significance for survival differences. The results suggest that the mutations of BCATGs may not play a key role in the adaptation of tumor acidosis.

Consensus clustering using the BCATG expression matrix partitioned TCGA-BRCA samples into two stable subtypes, with Subtype II exhibiting uniformly elevated expression of BCATGs, enrichment for more aggressive PAM50 subtypes (TNBC, HER2+, and Luminal B), and significantly inferior disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) relative to Subtype I. Notably, even within the clinically favorable Luminal A subgroup, BCATGs Subtype II identified patients with significantly worse DFI and PFI. Multivariate Cox regression confirmed that Subtype II retained independent prognostic value for DSS and PFI after adjustment for PAM50 subtype, clinical stage, and ER/PR status. These findings suggest that BCATGs-defined transcriptional states capture acidosis-related heterogeneity that may complement existing classification systems and aid in refining risk stratification for patients with benign subtypes, such as Luminal A subtype patient.

Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) of differentially expressed genes between BCATGs subtypes further illuminated the underlying biology. Subtype II displayed pronounced activation of cell division, chromosome segregation, mitotic spindle, and cell cycle pathways, together with Hallmark signatures of E2F targets, G2M checkpoint, MYC targets, mTORC1 signaling, and mitotic spindle. In parallel, downregulation of apical junction, early estrogen response, and TGF-β signaling pathways was observed. By interfering with p53/p21 and PLK/CDC25C pathways, the G2/M checkpoint control is relieved, the mitotic process is accelerated, and the proliferation advantage is provided for breast cancer cells [75]. Inhibitors targeting spindle motor proteins selectively kill CIN-positive breast cancer cells [76]. Such transcriptional reprogramming aligns with the adaptive proliferative and dedifferentiation pattern that acidosis is known to promote within the TME.

CIBERSORT results show that five immune cells are up-regulated in Subtype II, among which the increased abundance of follicular helper T cells promotes tumor progression [77]. Increased abundance of M1 macrophages can promote immune escape [78]. The increased abundance of resting NK cells reflects the exhaustion or inhibition of NK cell function [79]. Seven immune cells are down-regulated in Subtype II breast cancer patients, among which the reduction of resting dendritic cells suggests that the immune response is impaired [80]. The down-regulation of resting CD4⁺ memory T cells suggest an impaired adaptive immune memory pool [81]. In addition, lower immune/stromal/ESTIMATE scores, higher tumor purity, reduced IPS across checkpoint modalities, and elevated TIDE scores are indicative of greater immune evasion potential.

Drug sensitivity prediction via oncoPredict (GDSC2) identified 20 compounds with statistically significant differential sensitivities between subtypes. Subtype II showed heightened sensitivity to 17 agents—primarily cell cycle regulators (e.g., MK-1775, MK-8776, AZD6738), proteasome inhibitors, apoptosis inducers, and kinase inhibitors—whereas Subtype I was preferentially sensitive to only three compounds, including a CDK1 inhibitor and TGF-β/Wnt pathway modulators. These results provide a potential therapeutic strategy for BCATGs subtypes in treatment.

To derive a clinically applicable prognostic tool, LASSO-Cox regression distilled a five-gene signature (AURKA, CCNA2, CDC45, EXO1, KIF4A) whose risk score effectively stratified patients into high- and low-risk groups, and external validation across multiple GEO datasets and METABRIC. Time-dependent ROC curves indicated moderate-to-good predictive performance, particularly for DSS. Multivariate Cox analysis established CCNA2 and CDC45 as independent predictors of poor outcome after adjustment for clinical stage and PR status, while clinical correlation analyses linked higher risk scores to advanced stage, T/N stage, and ER/PR-negative status. The results of the external validation set suggested that the model had stability and accuracy. Time-dependent ROC curves indicated moderate-to-good predictive performance, particularly for DSS. Multivariate Cox analysis established CCNA2 and CDC45 as independent predictors of poor outcome after adjustment for clinical stage and PR status. CCNA2, a core cyclin regulating G1/S and G2/M transitions [82], has been implicated in colorectal and triple-negative breast cancer proliferation, migration, and invasion, with knockdown or CDK4/6 inhibition reversing malignant phenotypes [83, 84]. CDC45, a central component of the CMG helicase complex, facilitates DNA unwinding during replication [85] and promotes G2/M progression, stemness, and EMT in lung adenocarcinoma and hepatocellular carcinoma [86]; in triple-negative breast cancer lines, its knockdown suppresses growth, migration, and invasion [87].

Leveraging the independent prognostic roles of CCNA2 and CDC45, we used virtual screening on molecular docking, and 100 ns MD simulations with MM/PBSA binding free energy calculations. This pipeline identified 24 high-affinity dual-target candidates (binding energy < − 8 kcal/mol with hydrogen bonding to both proteins), among which four lead drugs (SBC-115337, CDK2-IN-4, Bractoppin, and Corylin) exhibited favorable structural stability, persistent hydrogen bonding, and low binding free energies in the final 30 ns trajectories. Although these in silico results are promising, they remain predictive and require experimental validation. SBC-115,337 is a benzofuran compound and belongs to PCSK9 inhibitor [88]. CDK2-IN-4 is a selective CDK2 inhibitor. Studies have shown that it can alleviate the drug resistance of apatinib resistant liver cancer cells [89]. Bractoppin is a BRCA1 carboxy terminal domain (BRCT) inhibitor that inhibits tumor progression in ovarian marginal tumor organoids [90]. Corylin is a bioactive compound isolated from Psoralea corylifolia, which has inhibitory effects on ovarian cancer, oral squamous cell carcinoma, osteosarcoma, and non-small cell lung cancer [91–94].

This study has certain limitations. The BCATGs were derived from public datasets and preclinical acidosis models; causal relationships with extracellular pH sensing in human tumors warrant direct functional validation in patient-derived models. Although the prognostic model and subtypes showed robust external validation, their utility across diverse populations and treatment contexts remains to be prospectively evaluated. Finally, the virtual screening and MD results constitute hypotheses that necessitate wet-lab confirmation of binding affinity, cellular potency, and in vivo efficacy.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

BCATGs

Breast cancer acidosis tolerance genes

BP

Biological processes

CC

Cellular components

DFI

Disease-free interval

DSS

Disease-specific survival

GDSC2

Genomics of drug sensitivity in cancer 2

GO

Gene ontology

GSEA

Gene set enrichment analysis

GSVA

Gene set variation analysis

KEGG

Kyoto encyclopedia of genes and genomes

MF

Molecular functions

MSigDB

Molecular signatures database

NEAA

Nonessential amino acids

OS

Overall survival

PFI

Progression-free interval

ROC

Receiver operating characteristic

SNVs

Single nucleotide variants

TME

Tumor microenvironment

Author contributions

Yi Li, Diheng Wu and Zhenyi Shi contributed equally to this article. Yi Li and Wenyue Deng collect the data. Yi Li, Diheng Wu and Zhenyi Shi performed the bioinformatics analysis. Yi Li, Qian Luo and Qinwen Liu performed the molecular dynamics simulation. Yi Li, Aiping Lu, Daogang Guan conceived the study. Yi Li, Weiguo Chen, Genggeng Qin and Daogang Guan designed the study. Aiping Lu and Daogang Guan supervised the study.

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 32070676, 32370683), the Natural Science Foundation of Guangdong Province (Grant Nos. 2021A1515010737, 2023A1515012902).

Data availability

The data sets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors have reviewed and approved the final manuscript. This study represents original research not previously published or under consideration elsewhere, in whole or in part.

Competing interests

The authors declare that they have no competing interests.