Expression analyses of fatty acid binding proteins (FABPs) in breast cancer subtypes: implications for immune modulation and clinical outcomes

Introduction

Fatty acid-binding protein (FABP) family is a group of proteins that play pivotal roles in cell metabolism and inflammation. FABPs facilitate the transportation of fatty acids between various cellular organelles and activate transcription factors (1,2). The FABP family comprises 12 proteins, of which nine members have been discovered in humans (FABP1, FABP2, FABP3, FABP4, FABP5, FABP6, FABP7, FABP9, and FABP12). Recently, the involvement of FABPs in cancer development and progression has been reported (3-5). However, the specific roles of FABPs in different types of cancers have not been clearly defined, and the expression patterns of distinct FABPs in various cancers have not been fully investigated.

FABPs bind to fatty acids and play roles in the storage and metabolism of lipids (1,2). Dysregulation of these proteins has been observed in various metabolic and immune disorders (6-8). However, the mechanisms by which FABPs are involved in the development and progression of these disorders are still not well understood. Recent research has revealed that FABP4 and FABP7 promote the progression of several types of cancer, including prostate, colon, breast, and cervical cancers (9-12). FABPs are expressed in macrophages within the cancer stroma and may mediate metabolic crosstalk between cancer cells and stromal tissues containing macrophages, adipocytes, and various other cells. FABPs in different cellular components within the tumor microenvironment contribute to cancer development and progression (13-15). Moreover, circulating FABPs (cFABPs), generated by various cell types, activate cancer cells. cFABPs may serve as biomarkers for cancer diagnosis and potential therapeutic targets (16).

Breast cancer is a complex and significant health issue affecting millions of individuals worldwide (17). Its importance stems not only from its high incidence but also from its diverse nature. Breast cancer is incredibly heterogeneous, encompassing various subtypes with distinct molecular profiles, clinical behaviors, and responses to treatment. Furthermore, breast cancer’s heterogeneity extends beyond molecular subtypes and encompass diverse genetic alterations, tumor microenvironments, and immune responses. Understanding this multifaceted disease is crucial for developing tailored strategies for prevention, early detection, and effective treatment (18,19).

Aberrant metabolic profiles may contribute to the heterogeneity of breast cancer (20). However, the mechanisms generating the diversity are not fully understood. Nevertheless, numerous evidences suggested that comprehending the metabolic profile of breast cancers may aid predicting treatment responses. Aberrant metabolic pathways confer resistance to chemotherapy and hormonal therapies (20-22).

In the current study, we investigated the expression patterns and prognostic significance of FABPs across different subtypes of breast cancer, utilizing large integrated datasets and validating the findings with breast cancer cell lines and human breast cancer tissues. We also examined the relationship between FABP expression patterns and prognostic values and explored their potential as immune signatures. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2483/rc).

Methods

Expression analysis of FABPs family in breast cancer

The online database (GEPIA, http://gepia.cancer-pku.cn/) was utilized to compare the expression of FABPs between normal and tumor tissues using The Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) data collection (23). Marker values (FABP expression levels) were analyzed as continuous variables. Expression data from the GEPIA/TCGA BRCA dataset were used without transformation, and group comparisons between tumor and normal tissues were conducted based on the normalized TPM values provided by the database. For further analyzing expression differences across breast cancer molecular subtypes, ER/PR status, and association with tumor grades, we employed Breast Cancer Gene-Expression Miner v5.1 (BC-GEM, bcGenExMiner v5.1, http://co.bmc.lu.se/gobo/gsa.pl) and conducted one-way analysis of variance (ANOVA) with post-hoc Tukey test (24). Moreover, stage plot function of GEPIA was applied to examine the correlations between FABPs expression and tumor stage. A Box and Whisker Plot was used to demonstrate the data distribution of gene expression through their quartiles. P-values from Dunnett-Tukey-Kramer pairwise multiple comparisons post hoc tests.

Survival analysis

The PROGgeneV2 online tool (http://genomics.jefferson.edu/proggene/) was used to explore the correlation between most significantly expressed FABPs and breast cancer and validate the differential prognostic values depending on subtype and treatment options (25). The Kaplan-Meier curve shows the cumulative survival probability, log-rank P value was displayed on the web page. P value <0.05 was considered statistically significant. Median expression is set as the separation point.

Functional enrichment analysis of differential gene expression (DEGs)

To analyze the biological processes affected by differential gene expression, DEGs was subjected to Gene Ontology (GO) enrichment analysis to evaluate biological processes, cellular components, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the linkedomics online analysis tool and CorrelationAnalyzeR web. P<0.05 indicated statistical significance (26,27).

Analysis of immune infiltration and immune checkpoints

Tumor Immune Estimation Resource Version 2 (TIMER2.0, http://timer.comp-genomics.org/), was used for the evaluation of relationships between expression of FABPs and immune infiltration across TCGA-BRCA tumor subtypes. Besides, the ‘gene correlation’ of FABPs in TCGA-BRCA was used to explore associations between FABPs and immune checkpoint using TISIDB (http://cis.hku.hk/TISIDB/) website tool. P-values and partial rho values were obtained by purity-adjusted Spearman’s rank correlation test. This site was also used to explore the role of FABPs and breast cancer immune classification (28,29).

Genetic alteration of FABPs based on TCGA BRCA project

The cBioPortal (https://www.cbioportal.org) was used to explore genetic alterations of FABPs in TCGA-BRCA (30).

Gene expression validation in breast cancer cell lines and human tissues

A retrospective, integrative bioinformatics analysis using publicly available transcriptomic datasets [TCGA and Gene Expression Omnibus (GEO)] was performed to identify differential expression patterns, and these findings were subsequently validated experimentally using human breast cancer cell lines. The breast cancer cell lines MCF7 and MDA-MB-231, along with the non-malignant breast epithelial cell line MCF10A, were obtained from the American Type Culture Collection (ATCC). MCF10A cells were used as a reference to represent non-malignant condition. Cells were maintained in appropriate culture media and grown under standard conditions of 37 ℃ and 5% CO₂.

Human breast cancer tissue samples were obtained from Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea, tissue samples were retrospectively collected between 2013 and 2019. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Ewha Womans University Mokdong Hospital (Doc. No. SEUMC202405036007-HE001), and written informed consent was obtained from all participants prior to sample collection. Eligible samples were obtained from adult female patients (≥18 years) diagnosed with primary operable invasive breast carcinoma who had available fresh-frozen tumor tissue and complete clinicopathological data, including ER, PR, HER2, Ki-67 index, histological grade, tumor size, and lymph node status. Fresh-frozen tumor specimens from primary breast cancers (total n=38) were retrospectively obtained using standard surgical and biobanking procedures, snap-frozen in liquid nitrogen immediately post-resection and stored at −80 ℃ until RNA extraction. For real time RT-PCR validation of subtype-specific expression, samples were distributed as follows: luminal A (n=9), luminal B (n=9), HER2-enriched (n=11), and basal like breast cancer (n=9). Clinicopathological data, including ER, PR, HER2, Ki-67 index, histological grade, tumor size, and lymph node status, were obtained from routine histopathological reports.

Total RNA was extracted from cell lines and tissues using the RNeasy Mini Kit (Qiagen, Hilden, Germany; Catalog No. 74104), following the manufacturer’s protocol. cDNA synthesis was performed with the iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA; Catalog No. 170-8891) in a T100™ Thermal Cycler (Bio-Rad). Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted on a QuantStudio™ Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) using SYBR Green Supermix (Thermo Fisher Scientific, Catalog No. 4309155). Relative gene expression of FABP4, FABP5, FABP6, and FABP7 was calculated using the ΔΔCq method, with GAPDH as the internal control. The CT values of the target genes were first normalized to the reference gene GAPDH. Subsequently, the ΔCT values of the breast cancer cell lines (MCF7 and MDA-MB-231) were normalized to those of the non-malignant MCF10A cell line to obtain the ΔΔCT values. Relative expression levels were calculated as fold changes and then rescaled based on log₁₀ transformation. Primer sequences were designed using Primer-BLAST (NCBI, USA) and synthesized by Bioneer (Daejeon, Republic of Korea), as detailed in Table S1. All assays for the measurements were performed in at least three independent replicates.

Statistical analysis

Two-tailed Wilcoxon rank-sum test was used to compare gene expression between normal and tumor groups. Spearman’s rank correlation test was used to test for correlations between variables. Correlation between FABPs expression and pathological stages of tumors was assessed. F value was used to determine the differences between each value and its group mean.

Differences in FABP4-FABP7 expression among luminal A, luminal B, HER2-enriched, and basal like subtypes in the tissue cohort were evaluated using ANOVA with post-hoc pairwise comparisons. Differences in gene expression between clinicopathological groups were assessed using the Wilcoxon rank-sum test. All statistical analyses were performed using R software, and P values <0.05 were considered statistically significant.

Results

Gene expression patterns of FABPs among different breast cancer subtypes

We analyzed expression patterns of nine isoforms of FABPs in humans, including FABP1-FABP7, FABP9-FABP12, in normal breast tissues and breast tumors using TCGA-BRCA database. We found that FABP4, FABP5, and FABP7 were significantly downregulated in tumor tissues, while FABP6 was significantly upregulated in tumor tissues, compared with normal breast tissues. Expression levels of FABP1, FABP2, FABP9, and FABP12 were lower than other isoforms of FABPs in both tumor and normal tissues (Figure 1A).

Figure 1 Gene expression profile of FABPs gene family in TCGA-BRCA. (A) Expression of FABPs gene family in breast cancer and normal breast tissues based on TCGA and GTEx database. (B-I) Expression of the FABP4, FABP5, FABP6 and FABP7 across breast cancer molecular subtypes based on BRCA TCGA using BC-GEM, bcGenExMiner v5.1 online tool. One-way analysis of variance with post-hoc Tukey test, *, P<0.05, **, P<0.01; ***, P<0.001. FABP, fatty acid-binding protein; GTEx, Genotype-Tissue Expression; TCGA-BRCA, The Cancer Genome Atlas Breast Invasive Carcinoma; RSEM, RNA-Seq by Expectation Maximization (expression quantification); IHC, immunohistochemistry.

We investigated differences in the expression of FABP4, FABP5, FABP6, and FABP7 according to ER/PR status and molecular subtypes of breast cancer using bc-GenExMiner v5.0. FABP4 expression was significantly higher in the luminal A subtype compared with basal-like, luminal B, and HER2-enriched subtypes (Figure 1B), and was also higher in ER/PR-positive tumors than in ER/PR-negative tumors (Figure 1C). FABP5 showed higher expression in the basal-like subtype (Figure 1D) and in ER/PR-negative breast cancers (Figure 1E). FABP6 expression was significantly higher in the HER2-enriched subtype (Figure 1F) and in ER/PR-negative tumors (Figure 1G). FABP7 exhibited a similar pattern to FABP5, with higher expression in the basal-like subtype (Figure 1H) and in ER/PR-negative breast cancers (Figure 1I).

Expression analysis using TNBC subclasses revealed that different types of breast cancers showed differential expressions of FABP5, FABP6, and FABP7. FABP5 was more expressed in TNBC-BL1 and FABP6 and FABP7 were more expressed in TNBC-LAR subtype (Figure S1).

Clinical significance of FABPs in breast cancer

We investigated if expressions of FABP4, FABP5, FABP6, and FABP7 are associated with survival using PROGgeneV2 web tool analysis. FABP4 expression in breast cancers was associated with shorter overall survival based on TCGA-BRCA [hazard ratio (HR) =1.18; 95% confidence interval (CI): 1.04–1.32, P=0.007, Figure 2A] and GSE19783-GPL6480 (HR =1.27; 95% CI: 1.01–1.59, P=0.04, Figure 2B). FABP5 was associated with shorter survival based on NKI (HR =1.9; 95% CI: 1.05–1.35, P=0.005, Figure 2C), GSE10893-GPL887 (HR =1.5; 95% CI: 1.14–2.1, P=0.004, Figure 2D), GSE18229-GPL887 (HR =1.5; 95% CI: 1.14–2, P=0.004, Figure 2E), GSE2607-GPL887 (HR =1.78; 95% CI: 1.17–2.71, P=0.007, Figure 2F) and GSE6130-GPL887 (HR =2.5; 95% CI: 1.33–4.73, P=0.004, Figure 2G). FABP6 was associated with shorter survival based on GSE19536 dataset (HR =1.22; 95% CI: 1.02–1.47, P=0.03, Figure 2H). FABP7 was associated with shorter survival based on GSE19536-GPL6480 (HR =1.14; 95% CI: 1.01–1.28, P=0.04, Figure 2I) and NKI (HR =1.1; 95% CI: 1.04–1.17, P=0.007, Figure 2J).

Figure 2 The overall survival analysis based on FABPs expression in various breast cancer datasets using the PROGgeneV2 online tool. Kaplan-Meier overall survival analysis based on FABP4 expression in the TCGA-BRCA cohort (A) and the GSE19783-GPL6480 dataset (B). Kaplan-Meier overall survival analysis based on FABP5 expression in the NKI cohort (C), GSE10893-GPL887 (D), GSE18229-GPL887 (E), GSE2607-GPL887 (F), and GSE6130-GPL887 (G). Kaplan-Meier overall survival analysis based on FABP6 expression in the GSE19536-GPL6480 (H). Kaplan-Meier overall survival analysis based on FABP7 expression in the GSE19536-GPL6480 (I) and NKI (J) cohorts. FABP, fatty acid-binding protein; TCGA-BRCA, The Cancer Genome Atlas Breast Invasive Carcinoma.

Analysis of FABP expression across breast cancers with different tumor stages was performed using GEPIA2. Log₂(TPM+1) gene expression data were analyzed by one-way ANOVA, with pathological stage as the grouping variable (Figure 3A-3D). FABP4, FABP6, and FABP7 were differentially expressed across tumor stages (FABP4: P<0.001; FABP6: P=0.040; FABP7: P=0.01; Figure 3). In contrast, FABP5 expression did not show significant variation among tumor stages (Figure 3B). For breast cancer grades (SBR), FABP4 was associated with low-grade tumors (SBR1 and SBR2) (P<0.001, Figure 3E), whereas FABP5, FABP6, and FABP7 were associated with high-grade tumors (SBR3) (P<0.001, Figure 3F-3H).We also found that FABP4 expression was higher in tumors with wild-type p53 and BRCA1 genes (P<0.001, Figure 3I,3J) than in tumors with mutated p53 and BRCA1. In contrast, FABP5 expression was elevated in tumors with p53 and BRCA1 mutations (P<0.001, Figure 3K,3L), whereas FABP6 expression did not differ significantly according to p53 or BRCA1 status (Figure 3M,3N). FABP7 expression was also higher in tumors with p53 and BRCA1 mutations (P<0.001, Figure 3O,3P).

Figure 3 FABPs expression among different tumor stages and grades. (A-D) Stage plot showing the correlation between FABPs gene expression and pathological stages of tumors based on BRCA TCGA. Correlation between FABPs expression and pathological stages of tumors was used in analysis of variance (The Kruskal-Walli’s test). F value was used to determine the differences between each value and its group mean. (E-H) FABPs family gene expression in patients with different SBR grades based on the bc-GenExMiner online software. P values from Dunnett-Tukey-Kramer pairwise multiple comparisons post hoc tests. Comparison of expressions of FABP4, FABP5, FABP6 and FABP7 between tumors with and without P53/BRCA1 mutation (I-P). The mRNA level of FABPs in breast cancers with different P53 status (Wild-type and Mutated gene). A Box and Whisker Plot was used to demonstrate the data distribution of gene expression through their quartiles. P values from Dunnett-Tukey-Kramer pairwise multiple comparisons post hoc tests. *, P<0.05, **, P<0.01; ***, P<0.001; NS, non-significant. BRCA1, breast cancer susceptibility gene 1; FABP, fatty acid-binding protein; P53, tumor protein p53; SBR, Scarff-Bloom-Richardson (histological grade); TCGA-BRCA, The Cancer Genome Atlas Breast Invasive Carcinoma.

Functional enrichment of FABPs genes

To investigate the potential functions of the FABPs family in breast cancer, functional enrichment and pathway enrichment analyses were performed using biological pathway database (BP). For FABP4, the enrichment analysis showed that FABP4-related genes were mainly involved in complement activation, humoral immune response mediated by circulating immunoglobulin, and regulation of humoral response (Figure 4A-4C). FABP5-related genes were primarily enriched in the establishment of protein localization to the endoplasmic reticulum, co-translational protein targeting to the membrane, and nuclear transcribed mRNA catabolic process nonsense-mediated decay (Figure 4D-4F). FABP6-related genes were mainly associated with mitochondrial translation termination, co-translational protein targeting the membrane, and translational termination (Figure 4G-4I). FABP7-related genes were primarily involved in complement activation, humoral immune response mediated by circulating immunoglobulin, and phagocytosis recognition (Figure 4J-4L). In addition, the Molecular Signatures Database (MSigDB) was used to analyze FABP-DGEs according to TCGA dataset. The most enriched pathway for FABP4 was NAKAYAMA_SOFT_TISSUE_TUMOR (Figure 5A). For FABP5, the most enriched pathway was VANTEER_BC_ESR1_DN (Figure 5B). For FABP6, the most enriched pathway was ANDERSEN_CHOLANGIOCARCINOMA (Figure 5C). For FABP7, the most enriched pathway was SMID_BREAST_CANCER_RELAPSE_IN_BONE_DN (Figure 5D).

Figure 4 Functional enrichment analysis of FABPs family using GSEA software. FABP4 (A-C), FABP5 (D-F), FABP6 (G-I) and FABP7 (J-L). FABP, fatty acid-binding protein; GSEA, gene set enrichment analysis.

Figure 5 Functional enrichment according to the MSigDB. GSEA based on TCGA and GTEx datasets showing significantly enriched pathways for FABP4 (A), FABP5 (B), FABP6 (C), and FABP7 (D) in breast cancer compared with normal breast tissues. The ES reflects the degree to which genes in each set are overrepresented at the top or bottom of the ranked gene list. ES, enrichment score; FABP, fatty acid-binding protein; GSEA, gene set enrichment analysis; GTEx, Genotype-Tissue Expression; MSigDB, Molecular Signatures Database; TCGA, The Cancer Genome Atlas.

Analyzing correlations among FABPs expression and immune cell infiltration and immune checkpoints

Analysis of correlation between FABPs expression and immune cell infiltration in breast cancers was carried out using BRCA-TCGA data in CIBERSORT, CIBERSORT-ABS, TIMER, TIDE, XCELL, QUANTISEQ, MCPCOUNTER, and EPIC algorithms. In luminal A subtype, FABP4 expression was inversely correlated with tumor purity, indicating higher immune cell infiltration. Particularly, it was associated with increased infiltration of CD8⁺ T cells, CD4⁺ T cells, and macrophages (Figure 6A). In luminal B subtype, FABP4 expression was inversely correlated with tumor purity and associated with increased infiltration of CD4⁺ T cells, macrophages, and cancer-associated fibroblasts (Figure 6B). In HER2-E subtype, FABP4 expression was inversely correlated with tumor purity. Particularly, it was associated with increased infiltration of CD8⁺ T cells, CD4⁺ T cells, and macrophages (Figure 6C). In basal subtype, FABP4 expression was not associated with tumor purity nor immune cell infiltration (data not shown).

Figure 6 Association between estimated immune infiltrates and FABPs gene expression. (A-C) FABP4 expression correlated with tumor purity and immune cell infiltration across breast cancer subtypes: luminal A (A), luminal B (B), and HER2-enriched (C). (D-F) FABP5 expression correlated with tumor purity and immune cell infiltration in luminal A (D), luminal B (E), and HER2-enriched (F) breast cancers. (G,H) FABP7 expression correlated with tumor purity and immune cell infiltration in luminal A (G) and basal-like (H) breast cancers. CIBERSORT-ABS, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts—absolute mode; EPIC, Estimating the Proportions of Immune and Cancer cells; FABP, fatty acid-binding protein; HER2, human epidermal growth factor receptor 2; MCPcounter, Microenvironment Cell Populations-counter; NK, natural killer; TIMER, Tumor Immune Estimation Resource; xCell, gene-signature-based immune cell estimation method.

Likewise, FABP5 expression displayed a negative correlation with tumor purity across different breast cancer subtypes. In luminal A, FABP5 expression was associated with increased infiltration of CD8⁺ T cells, CD4⁺ T cells, macrophages, cancer-associated fibroblasts, and NK cells (Figure 6D). Luminal B subtype with FABP5 expression exhibited notable infiltration of macrophages (Figure 6E). However, in basal subtype, FABP5 expression did not correlate with tumor purity (data not shown). In HER2-E subtype, FABP5 expression was inversely correlated with tumor purity. Particularly, it was associated with increased infiltration of CD8⁺ T cells and macrophages (Figure 6F).

FABP6 expression did not show a significant association with tumor purity across breast cancer subtypes (data not shown). FABP7 expression exhibited negative correlation with tumor purity in luminal A, and positive correlations with CD8⁺ T cells. In the basal subtype, FABP7 expression showed positive correlations with CD8⁺ T cells and NK cells (Figure 6G,6H).

By using the TISIDB website, correlation between FABPs and most common immunomodulators in breast cancers has been investigated. Significant positive relationships were observed between FABP4 and CD244 (P=0.02) and CD160 (P<0.001), whereas the association with CTLA4 was weak (P=0.01) (Figure 7A-7C). FABP5 was positively associated with CD274 (P<0.001 for all; Figure 7D-7G). FABP6 was positively associated with CTLA4 and LAG3 (P<0.001, Figure 7H,7I). FABP7 was positively correlated with CD244 (P<0.001), CTLA4 (P<0.001), and LAG3 (P<0.001) (Figure 7J-7L). Overall, the findings suggest a complex relationship between FABP expression and immune cell infiltration in breast cancer, highlighting the potential roles of FABPs in modulating the tumor microenvironment and immune responses across different molecular subtypes. Additionally, FABP expression is associated with immune checkpoint molecules in breast cancer.

Figure 7 Correlation between FABPs and immune modulators. (A-C) FABP4 expression correlated with immune checkpoint molecules CTLA4 (A), CD160 (B), and CD244 (C) in TCGA-BRCA. (D-G) FABP5 expression correlated with CD274/PD-L1 (D), CTLA4 (E), CD244 (F), and LAG3 (G). (H,I) FABP6 expression correlated with CTLA4 (H) and LAG3 (I). (J-L) FABP7 expression correlated with CD244 (J), CTLA4 (K), and LAG3 (L). Correlation analyses were performed using the TISIDB database based on TCGA-BRCA data. Spearman’s rank correlation coefficient (rho) and corresponding p values are shown in each panel. BRCA, breast invasive carcinoma; CD274/PD-L1, programmed death ligand 1; CD244, cluster of differentiation 244; CD160, cluster of differentiation 160; CTLA4, cytotoxic T-lymphocyte-associated protein 4; FABP, fatty acid-binding protein; LAG3, lymphocyte activation gene 3; rho, Spearman’s correlation coefficient; TCGA, The Cancer Genome Atlas.

The frequency of genetic alteration among FABPs gene family in breast cancers

We investigated genetic mutation of FABPs in TCGA-BRCA using cBioportal database. We found that the frequency of genetic alteration among FABPs gene family in breast cancers was very low (less than 5%). Most genetic alteration was genetic amplification (FABP1, 0.4%; FABP2, 0.3%; FABP3, 0.6%; FABP4, 5%; FABP5, 5%; FABP6, 0.6%; FABP7, 0.9%; FABP9, 5%; FABP12, 5%) (Figure S2).

Validation of FABPs expression in breast cancer cell lines

Using real-time RT-PCR, we identified significant differences in the expression of four selected genes—FABP4, FABP5, FABP6, and FABP7—across three cell lines: MCF10A (non-malignant breast cells), MCF7, and MDA-MB-231. Relative normalization to the expression level in the non-malignant MCF10A cell line revealed that FABP4 expression was drastically reduced in both MCF7 (8.88×10⁻⁶) and MDA-MB-231 (5.86×10⁻⁶). FABP5 expression was also decreased, with MCF7 showing a 0.0135-fold level and MDA-MB-231 showing a slightly higher 0.0290-fold level. In contrast, FABP6 exhibited markedly higher expression, with a 7.46-fold increase in MCF7 and a 2-fold increase in MDA-MB-231 compared to MCF10A. For FABP7, expression levels in MCF7 and MDA-MB-231 were 0.153- and 0.072-fold, respectively, relative to MCF10A (Figure 8A).

Figure 8 Validation of FABP gene expression in breast cell lines. (A) Relative expression of FABP genes in MCF10A, MCF7, and MDA-MB-231 cell lines. Quantitative real-time polymerase chain reaction analysis of FABP4, FABP5, FABP6, and FABP7 expression levels in the non-malignant breast epithelial cell line MCF10A and breast cancer cell lines MCF7 and MDA-MB-231. Data are represented as log₁₀ fold changes relative to MCF10A, with GAPDH as an internal control. (B) Comparison of FABP gene expression between MCF7 and MDA-MB-231 cell lines. Relative expression levels of FABP4, FABP5, FABP6, and FABP7 in MDA-MB-231 compared to MCF7. Data are expressed as log₁₀ fold changes calculated using the ΔΔCq method. ***, P<0.001 as determined by Student’s t-test. FABP, fatty acid-binding protein.

Additional analysis of FABP gene expression in MDA-MB-231 cells relative to MCF7 revealed further differences in gene regulation between these two breast cancer cell lines. FABP4 expression in MDA-MB-231 was reduced to 0.660-fold compared to MCF7. In contrast, FABP5 expression was elevated, with MDA-MB-231 exhibiting a 2.144-fold increase over MCF7. Both FABP6 and FABP7 were downregulated in MDA-MB-231, with expression levels of 0.268- and 0.467-fold, respectively, relative to MCF7. These findings highlight distinct expression patterns of FABP genes between MCF7 and MDA-MB-231 (Figure 8B).

Validation of FABPs expression in breast cancer tissues

In tissue validation by RT-PCR, no statistically significant differences in FABP4-FABP7 mRNA expression were observed across breast cancer subtypes, despite visually apparent subtype-related trends (Figure 9A). All tissue samples were obtained from female patients with a mean age of 52±11.5 years. ER (−) cancer tissues exhibited higher FABP5 expression than ER (+) tissues with marginal significance (P=0.050). FABP4 and FABP6 showed slight differences according to ER status, and FABP4, FABP5, and FABP6 showed slight differences between PR (+) and PR (−) cancers, although none of these differences reached statistical significance (Figure 9B,9C). Clinicopathological analysis revealed no significant differences in the expression of FABP4, FABP5, FABP6, or FABP7 between low-grade (SBR grade 1–2) and high-grade (SBR grade 3) tumors (Figure 10A). Notably, FABP4 expression was significantly higher in larger tumors (>2 cm) compared with smaller tumors (≤2 cm, P=0.01) (Figure 10B). FABP7 expression also tended to be elevated in tumors with a high Ki-67 index (≥20%) relative to those with lower Ki-67 levels, reaching borderline statistical significance (P=0.047), which may indicate a potential association with tumor proliferative activity (Figure 10C). No significant differences in FABP expression were observed according to lymph node status (Figure 10D).

Figure 9 FABP4-FABP7 mRNA expression in breast cancer tissues. (A) Relative mRNA expression levels of FABP4, FABP5, FABP6, and FABP7 across breast cancer RSCMC subtypes. (B) Relative mRNA expression levels of FABP4-FABP7 according to ER status (ER-negative vs. ER-positive). (C) Relative mRNA expression levels of FABP4-FABP7 according to PR status (PR-negative vs. PR-positive). ER, estrogen receptor; PR, progesterone receptor.

Figure 10 Association between FABP4-FABP7 expression and clinicopathological parameters. (A) Relative mRNA expression levels according to SBR tumor grades. (B) Relative mRNA expression levels according to tumor size (small vs. large). (C) Relative mRNA expression levels according to the Ki-67 proliferation index. (D) Relative mRNA expression levels according to lymph node status (no vs. yes). Cutoff points were defined as follows: tumor grade (low grade, grades 1–2; high grade, grade 3), tumor size (small tumor ≤2 cm; large tumor >2 cm), lymph node status (absence vs. presence of lymph node metastasis), and Ki-67 (low <20%; high ≥20%).

Discussion

Reprogramming of metabolism is known to promote tumor progression. Heterogeneous metabolic changes are found in different types of tumors and different subtypes. The metabolic changes within tumor cells and tumor microenvironment promote tumor growth. For instance, several studies have shown that cancer cells exhibit increased glucose uptake and utilization, known as the Warburg effect, to meet their high energy demands (31-33). Targeting these metabolic reprogramming as inhibiting glucose metabolism has been suggested a way to overcome treatment resistance (33).

Breast cancer is characterized by its heterogeneity generated with aberrant metabolic pathways. Therefore, understanding the metabolic alterations in breast cancer may enable us to identify potential metabolic targets for personalized therapy (19,20). FABPs are key fatty acid transporters known to control several lipid metabolic pathways. Nevertheless, roles of these transporters in cancers have not been fully elucidated (2). Our analysis revealed that FABPs expression patterns are distinct in different subtypes of breast cancer (Figure 1). Differences in mRNA expression of FABPs among breast cancer subtypes suggest that FABPs may play different roles and result in different metabolic outcomes (Figure 1).

We verified FABPs expression patterns across breast cancer subtypes. FABP4 is highly expressed in luminal A subtype and ER+/PR+ cancers. Additionally, in luminal A, FABP4 is related with immune cell infiltration. Our finding corresponds to Hao et al.’s previous report showing that FABP4 in tumor associated macrophages regulates inflammatory responses through NFkB/miR-29b pathway and promotes breast cancer growth (34). In addition, Hao et al. reported that circulating adipose FABP promotes obesity-associated breast cancer development with activating IL-6/STAT3/ALDH1 pathway (34). Association of FABP4 and ER has been suggested. Gharpure et al. found that selective targeting of ER alpha by tamoxifen reduces ovarian cancer metastasis and progression with decreasing FABP4 expression (35). Similarly, Esteruelas et al. found that exogenous FABP4 affects oncogenic signals in MCF7 cells (Luminal A cell line) but not in MDA-MB231 cells (36). Conversely to our findings, Kim et al. demonstrated that FABP4 is overexpressed in HER2-enriched subtype while they reported that FABP4 is downregulated in luminal A (37). Overall, while TCGA data provides valuable insights into cancer biology, meta-analyses and validation studies across multiple datasets may be essential to interpret discrepancies and enhance our understanding of the complex interplay between gene expression and breast cancer.

FABP5 and FABP7 exhibit similar expression patterns in breast cancer tissues, particularly being linked to basal subtypes, ER−/PR− status and immune cell modulators. Powell et al. have revealed that FABP5 plays a substantial role in TNBC proliferation by mediating EGFR aberrant expression (38). Similarly, it was reported that FABP7 was related with TNBC growth signal, mediating cancer cell metabolic reprogramming (39,40). Roles of FABP5 and FABP7 in cancer progression have been reported in prostate, melanoma, and lung cancer, as well (41,42).

FABP6 is associated with the HER2-enriched subtype and ER−/PR− status. Among TNBC subtypes, FABP6 is more highly upregulated in the TNBC-LAR subtype (Figure S1). However, previous studies examining FABP6 expression across different breast cancer molecular subtypes have been limited. Therefore, future research should include FABP6 as a gene of interest for differential expression analysis among breast cancer subtypes. Notably, FABP6 has been reported to promote cancer progression in colorectal cancer, glioma, and bladder cancer (43-45).

Additionally, we found that FABP4, FABP5, and FABP7 are closely associated with immune cell infiltration, particularly CD8⁺ T cells. Additionally, these proteins showed strong associations with key immune modulators, including CTLA4 and PD-L1 (Figure 4). These findings suggest that FABPs may play a role in immune regulation within breast cancer.

FABPs have recently been implicated in the regulation of immune cell metabolism, although mechanistic evidence remains limited (46). FABP4, highly expressed in macrophages, links lipid metabolism to inflammatory signaling and has been shown to promote obesity-associated pancreatic cancer through activation of the NLRP3/IL-1β pathway (47). FABP5 supports fatty-acid transport and mitochondrial metabolism in T cells and has been identified as an immunometabolic marker that enhances T-cell survival in lipid-rich tumor environments (48). In addition, FABP5 and FABP7 have been associated with metabolically stressed CD8⁺ T cells and tumor-associated macrophages, respectively, where they contribute to immunosuppressive phenotypes (49,50). Together, these findings suggest that dysregulated FABP expression promotes lipid-driven metabolic programs that support pro-tumorigenic and immunosuppressive functions within the tumor microenvironment.

We validated gene expression in breast cancer cell lines (MCF7 and MDA-MB-231) and the non-malignant breast epithelial cell line MCF10A, observing both consistencies and discrepancies when compared to tissue data from large-scale dataset analyses. For instance, FABP4 was markedly downregulated in both cancer cell lines relative to MCF10A, consistent with its reduced expression in tumor tissues. Similarly, FABP5 expression was lower in both MCF7 and MDA-MB-231 compared to MCF10A, with MDA-MB-231 exhibiting slightly higher levels than MCF7. FABP6, on the other hand, showed significant upregulation in MCF7 and a moderate increase in MDA-MB-231, reflecting its elevation in tumor tissues.

We further compared the expression levels of FABP genes between MDA-MB-231 and MCF7 cell lines. FABP4 expression was significantly downregulated in MDA-MB-231 compared to MCF7, consistent with tissue data showing lower FABP4 expression in basal-like subtypes. Similarly, FABP5 was relatively upregulated in MDA-MB-231, aligning with its expression pattern in tumor tissues. However, in contrast to tissue data, FABP7 was downregulated in MDA-MB-231 relative to MCF7.

These cell line results reveal distinct expression patterns of FABP genes between luminal (MCF7) and basal (MDA-MB-231) breast cancer subtypes, supporting our findings from tissue analyses using TCGA data. Notably, discrepancies between cell line and tissue data—such as the differential expression of FABP7—may be influenced by the tumor microenvironment, which comprises a heterogeneous mix of cell types. Our analysis indicated that FABP7 expression is inversely correlated with tumor purity, suggesting that its expression may originate from surrounding non-cancerous cells, including immune cells within the tumor microenvironment.

Although subtype-specific expression patterns of FABP4, FABP5, FABP6, and FABP7 observed in TCGA datasets were broadly consistent with trends observed in vitro, tissue-level validation did not demonstrate statistically significant differences in relative mRNA expression across luminal A, luminal B, HER2-enriched, and basal-like breast cancer subtypes (Figure 9), likely reflecting the limited cohort size and substantial intra-subtype variability. Notably, clinicopathological association analyses revealed that FABP4 expression was significantly higher in larger tumors (P=0.01), while FABP7 expression was elevated in tumors with a high Ki-67 index (≥20%; P=0.047), suggesting a potential association with proliferative activity of cancer cells (Figure 10). FABP5 showed a marginal association with ER-negative status (P=0.05, Figure 9). Importantly, these clinicopathological trends are concordant with survival analyses and tumor grade-associated expression patterns observed in large-scale public datasets (Figures 2,3), supporting the biological relevance of FABP dysregulation in breast cancer.

However, our tissue validation findings should be interpreted cautiously, with emphasis on observed biological trends and effect sizes rather than definitive clinical conclusions. Future studies involving larger cohorts and advanced approaches, such as single-cell and spatial transcriptomics, will be required to more clearly distinguish tumor-intrinsic expression patterns from microenvironment-related influences across breast cancer subtypes.

Taken together, these observations, in conjunction with large-scale bioinformatic analyses, suggest that FABP4, FABP5, FABP6 and FABP7 may be associated with breast cancer subtype heterogeneity and warrant further investigation as candidate biomarkers within molecular classification frameworks.

Conclusions

This analysis confirmed the differential expression of FABPs across various molecular subtypes of breast cancer and suggests that distinct FABPs may be associated with specific metabolic signatures. Furthermore, the correlation between FABP expression and immune cell infiltration in different subtypes highlights a potential link between cancer cell metabolism and tumor immunity. Together with previous reports, these findings underscore the role of FABPs in breast cancer biology and support their potential use as subtype-specific biomarkers and therapeutic targets, warranting further investigation into their mechanistic functions and clinical value in treatment stratification and disease management.

Acknowledgments

This work partially contained Eman Taha Ali’s thesis of the Ph.D. degree in Ewha Womans University.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2483/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2483/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2483/prf

Funding: This study was supported by Leading Convergence Research Grant and Academic Research Grant provided by Ewha Womans University.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2483/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. The study was approved by the Institutional Review Board of Ewha Womans University Mokdong Hospital (Doc. No. SEUMC202405036007-HE001), and written informed consent was obtained from all participants prior to sample collection.

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/.

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