Integrative Raman Spectroscopy and Multi-Omics Analysis of Lipid and Matrix Protein Heterogeneity in Triple-Negative Breast Cancer

Abstract

Fatty acid accumulation and lipid alterations play crucial roles in the proliferation and progression of breast cancer cells. In this study, we investigated molecular changes in triple-negative breast cancer (TNBC) tumors compared with matched normal breast tissues collected from three patients. Tumor and normal samples were analyzed using Raman microspectroscopy integrated with liquid chromatography–mass spectrometry (LC–MS) to identify ω-3, ω-6, and ω-9 fatty acids and key proteins, including collagen types I, III, IV, and V; fibronectin; and laminin. Additionally, proteomics and lipidomics analyses were conducted to characterize chemical and metabolic alterations between TNBC and normal breast adipose tissues. Raman spectral analysis showed a significant increase in unsaturated lipids in TNBC tissues compared to matched control tissues. The direct classical least-squares (DCLS) approach was used to analyze Raman spectra collected from entire tissues to identify unsaturated fatty acids (including ω-3 and ω-6) and proteins from both cancerous and matched normal breast tissues obtained from the same patients. Both Raman score data from DCLS analysis and LC–MS lipid analysis demonstrated an increase in the levels of eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), arachidonic acid (AA), linoleic acid (LA), ω-3, and ω-6 and the ratio of LA/α-LA in tumors derived from TNBC compared to matched control tissues. Protein score data from DCLS analysis were consistent with our proteomics results for all proteins evaluated. The levels of collagen I (COL I), collagen III (COL III), collagen IV (COL IV), and laminin (LAM) were decreased in the TNBC tumor samples, while the levels of collagen V (COL V) and fibronectin (FN) were increased in the TNBC tumor samples. Lipidomics results showed that phosphatidylinositol (PI), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG) lipid species were upregulated in TNBC. Network and pathway analyses have shown that coordinated changes in lipid metabolism and extracellular matrix (ECM) remodeling are key features of TNBC. This study designed here demonstrates the accuracy of Raman microspectroscopy as a nondestructive method for spatial profiling of lipid and protein expression on tumor slides. Further, due to the heterogeneity of breast cancer tumors and the role of the tumor microenvironment (TME) in breast cancer progression, spatial mapping enables mechanisms for profiling key extracellular components that may drive breast cancer progression and intratumor heterogeneity.

Introduction

Breast cancer (BC) is one of the most prevalent types of cancer among women and is the top cause of cancer-related deaths worldwide. According to the World Health Organization (WHO), in 2022, 2.3 million incidences and 670,000 deaths were reported worldwide. Breast cancer is widely recognized as a heterogeneous disease, characterized by a broad range of clinical, pathological, and molecular properties. Based on immunohistochemical features, it is classified into three main subtypes according to intra- and extracellular domain receptors: estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Triple-negative breast cancer (TNBC) is characterized by a lack of ER/PR expression and the absence of HER2 amplification, accounting for ∼10–20% of breast cancer cases. TNBC is characterized by high metastasis rates, resistance to most adjuvant and neo-adjuvant therapies, and a lack of targetable receptors for traditional therapies. One issue in accurately targeting TNBC is its heterogeneity, both within and between tumors. TNBC heterogeneity can arise from interactions of cancer cells with the tumor microenvironment (TME), which includes diverse cellular populations, metabolites, lipids, and the extracellular matrix (ECM). Recently, both lipids and ECM have gained interest as novel regulators of tumor heterogeneity and mediators of breast cancer progression to a more aggressive phenotype. −

Several types of cancers are associated with alterations in the biochemical characteristics of cells. For example, compared to nontumor cells, lipid metabolism in cancer cells is elevated to fulfill the biogenesis, bioenergetics, and metabolic demands of tumors, thereby promoting cell proliferation and survival. The association between altered metabolism and cancer is generally described by glycolysis, generally known as the Warburg effect. − However, recent research has shown that other pathways, especially those related to lipoprotein metabolism and fatty acid production, may contribute to cell proliferation by providing the energy and membrane components necessary for rapid growth. ,−

Lipid biosynthesis has been reported in cancerous tissues to support the rapid proliferation of cancer cells. , However, fatty acid (FA) and cholesterol biosynthesis primarily occur in the liver, adipose tissue, and lactating breast tissues. − Studies have shown that cancer cells undergo a transition from lipid uptake to de novo lipogenesis. , This could alter the cell membrane lipid saturation, , leading to modifications in the levels of saturated and monounsaturated phospholipids, − which may protect cancer cells from oxidative damage by decreasing lipid peroxidation. , Aggressive breast cancer has shown elevated levels of saturated FAs, , indicating that decreased membrane fluidity may be a characteristic of advanced stages of the disease. A rise in polyunsaturated fatty acids (PUFAs) has been correlated with aggressiveness in cancer cell lines and the enhancement of tumor development. In addition to lipid metabolism, the TME plays a significant role in cancer progression.

The microenvironment comprises different cell types, including fibroblasts, immune cells, adipocytes, and endothelial cells, all of which are supported by the ECM. Components of the ECM provide cells with biomechanical and biochemical cues and play a crucial biological role in breast cancer progression and metastasis. , The ECM comprises various proteins including laminins, fibronectin, collagens, proteoglycans, glycosaminoglycans, matricellular proteins, and ECM remodeling enzymes. , There are distinct differences between normal and cancerous ECM compositions because of cancer progression. Mammary gland involution, a tissue remodeling procedure, is characterized by major changes in the ECM, which involve a pronounced increase in fibrillar collagens, fibronectin, matricellular proteins, and numerous ECM remodeling enzymes. Changes in the ECM can be recognized as a cancer hallmark.

Chemical imaging using Raman spectroscopy provides a label-free, spatially resolved analysis of lipid composition and distribution, offering insights into dynamic metabolic changes that immunohistochemistry (IHC) cannot capture due to its reliance on predefined markers. By directly mapping lipid heterogeneity in the TME, Raman-based spatial lipidomics can uncover the metabolic signatures of cancer progression, resistance mechanisms, and functional diversity, advancing our understanding of cancer biology beyond static protein markers. Confocal Raman microspectroscopy is a nondestructive, quantitative, and label-free method that has been applied in many biological studies for analyzing chemical composition. , Raman spectroscopy provides vital biological information and chemical changes associated with a disease, as each molecule displays a distinct pattern of vibrations, which can be used as a Raman biomarker. , This nondestructive method provides chemical information and the spatial distribution of components, such as proteins and lipids, in tissues. Moreover, in comparison to other common methods (e.g., liquid chromatography–mass spectrometry (LC–MS) and proteomics analysis), the samples do not require preparation and are not destroyed during analysis.

This study aims to leverage Raman microspectroscopy to investigate FA and ECM alterations in the TME of triple-negative human breast cancer compared with matched breast adipose tissue. Raman spectra and Raman images were collected from both cancerous and matched breast tissues, providing a basis for detailed biochemical comparisons. Ratio analysis of the Raman spectra revealed higher levels of unsaturated fatty acids in cancerous tissues compared to their matched nontumor counterparts. The spatial distribution of FAs and collagens within tissues was visualized using the DCLS (direct classical least-squares) method applied to Raman images.

To further elucidate the biochemical landscape, DCLS analysis was performed on Raman spectra to investigate changes in ω-3 and ω-6 FAs, which were subsequently correlated with the LC–MS results for cross-validation. Protein changes between the two tissue groups were explored by combining the DCLS analysis of Raman spectra with proteomics data for comprehensive validation. This integrative approach highlights the potential of Raman microspectroscopy not only for studying FA and protein alterations in cancerous tissues but also for identifying novel biomarkers and gaining insights into TME dynamics. Additionally, the methodology could be extended to study other tumor types and explore spatial ECM and metabolic changes under diverse pathological conditions.

Experimental Methods

Materials

α-Linolenic acid (CAS #822-18-4), docosahexaenoic acid (CAS #6217-54-5), docosapentaenoic acid (CAS #24880-45-3), eicosapentaenoic acid (CAS # 10417-94-4), stearidonic acid (CAS # 20290-75-9), arachidonic acid (CAS #506-32-1), linoleic acid (CAS #60-33-3), oleic acid (CAS #112-80-1), and cholesterol (CAS #57-88-5), cholesteryl palmitate (CAS #601-34-3), and 1,2-dipalmitoyl-sn-glycero-3-phosphate sodium salt (CAS #169051-60-9) were obtained from Cayman Chemical, and glyceryl tripalmitate (CAS #555-44-2) was purchased from Sigma-Aldrich. Collagen I (CAT 354249), fibronectin (CAT 354008), and laminin (CAT 354232) were purchased from Corning Life Sciences. Collagen III (CAT #5019-10MG) and collagen IV (CAT #5022-5MG) were purchased from Advanced BioMatrix. Collagen V (CAT #1270-01S) was purchased from Southern Biotech.

Preparation of Triple-Negative Breast Cancer (TNBC) Tissue

Frozen tissue sections from three patients were purchased from OriGene Technologies and stored at −80 °C until further use. For each patient, tumor and matched breast adipose tissue sections (5 × 5 μm²) collected from the same patient were mounted onto slides with an optimal cutting temperature (OCT) embedding medium. After thawing, the OCT was removed by washing the tissues twice with 70% cold ethanol, each for 30 min. Following this, the tissues were shaken in 1× PBS for 10–15 min to eliminate any residual ethanol. Tumor donor demographics are presented in Table .

1. Demographic Information of Patients.

patient ID	tissue	age	race	pathology verification	minimum stage grouping	% normal	% lesion	% tumor	BMI (kg/m*m)
FR00043520	TNBC	68	white	adenocarcinoma of the breast, ductal	I	0	0	30	not reported
FR0004352D	matched normal			within normal limits	I	100	0	0	not reported
FR000256AB	TNBC	71	black	adenocarcinoma of the breast, ductal	IIIB	0	0	20	33.4
FR000256A9	matched normal			within normal limits	IIIB	100	0	0	33.4
FR5B33BD81	TNBC	54	black	carcinoma of the breast, squamous cell	IIB	55	0	25	not reported
FR0002F9F3	matched normal			within normal limits	IIB	100	0	0	not reported

Histology

Hematoxylin and eosin (H&E) dyes were used to stain tissue sections for histological examination. Staining was performed on a Leica ST5020/CV5030 autostainer (Leica Biosystems) using Eosin Phloxine 515, SelecTech Hematoxylin 560, bluing, and define solutions (Leica Biosystems). The stained tissues were mounted using Surgipath Sub-X mounting medium and scanned with a Hamamatsu NanoZoomer HT whole slide scanner, which features an Olympus 20x/0.8 NA dry objective.

Proteomics

A comprehensive explanation of the proteomics method employed in this study can be found in the referenced paper . The details of the procedure can be found in the Supporting Information.

Lipidomics

A comprehensive explanation of the lipidomics method employed in this study can be found in the referenced paper . The details of the procedure can be found in the Supporting Information.

Raman Spectroscopy and Microscopy Experiments

Raman spectra from TNBC tissues mounted on glass slides were collected by a Renishaw inVia Reflex spectrometer using a 532 nm laser, 50XL air objective, 1800 grating at 50% power (15 mW), 10 s exposure, and 100–3200 cm^–1 spectral range. In each group of collected tissues (n = 3), for both normal and cancerous tissues, a total of 180 Raman spectra were collected. Raman images of OCT tissues at a spatial resolution of 100 μm × 100 μm were acquired using StreamHR mode, a 532 nm laser, 50XL air objective, 1800 grating at 100% power (30 mW), and 1 s exposure time at 1000 cm^–1 center. Raman spectra of lipid and protein standards in the form of liquid or powder were acquired using a 785 nm laser, 50XL air objective, 1200 grating, 10 s exposure time, and 10% power (18 mW) for lipids and 50% power (90 mW) for proteins. Thirty Raman spectra were acquired from each tissue type.

Statistical Analysis and Data Processing

To perform statistical analysis, first, the collected Raman spectra were baseline-subtracted using Renishaw’s WiRE 4.4 software’s “Intelligent Fitting”. OriginLab software (OriginLab, Northampton, MA) was used for plotting the spectra, data processing, and principal component analysis. DCLS was utilized for generating the mapping data. For this purpose, unprocessed Raman data were modified to eliminate the sudden and intense spikes caused by the cosmic rays. After cosmic ray removal, data were smoothed using a Savitzky–Golay process with a window size of 7 and a polynomial order of 2. Finally, corrected data were baseline-subtracted using Renishaw’s WiRE 4.4 software’s “Intelligent Fitting”. A comprehensive explanation of the DCLS analysis employed in this study can be found in the referenced papers , .

Results

To distinguish regions of cancer cells from the surrounding stroma on the TNBC tumor slides, we used H&E-stained images (Figure ). H&E staining of the patient tissue slides indicated varied levels of neoplasia and changes compared to the matched breast tissues.

1.

Hematoxylin and eosin (H&E)-stained patient tissue sections representing structural changes in (A–C) TNBC and (D–F) matched adipose tissue. Purple ellipses and yellow arrows indicate regions of interest, e.g., increased neoplasia or abnormal, potentially hyperplastic ducts. (A) Adenocarcinoma of breast, ductal; (B) adenocarcinoma of breast, ductal; and (C) carcinoma of breast, squamous cell. Comparison of the nucleus/cell area ratio between TNBC and matched breast tissues for (G) patient 1, (H) patient 2, and (I) patient 3. Scale bars are 1000 μm.

For example, Figure A shows an increased presence of tumor cells toward the lower portion of the tissue (purple ellipse) and arranged in nests and anastomosing cords. Additionally, there was a large blood vessel with a mass of tumor cells localized in the lumen (yellow arrow, bottom right of the tissue). Compared to the matched breast tissue from the same patients (Figure D), there are only a few ducts with neoplastic epithelium (yellow arrows). The other two tumor slides had similar features with regions of neoplastic duct epithelium toward the bottom of the tissue (yellow arrows, Figure B) and increased tumor cell density on the right side of the tissue (purple ellipse, Figure C). Corresponding matched breast tissues exhibited much lower/smaller neoplastic duct epithelium regions (purple ellipses). In addition, calculation of the nucleus/cell area ratio revealed higher levels for the TNBC tissues compared to the matched breast tissues (Figure G–I).

To understand the chemical composition within breast adipose tissue and cancerous tissues, we collected Raman spectra from both TNBC and matched breast tissues; Figure A shows average Raman spectra for all groups (n = 3, 30 spectra/patient). The first column of Figure S1A–C shows average Raman spectra for each group. In both groups of normal and cancerous tissues, bands at around 1003 cm^–1 (Phenylalanine; CH₃ rocking coupled with C–C stretching of carotenoids), 1097 cm^–1 (O–P–O (stretching PO₂ symmetric (phosphate II) of phosphodiesters), 1156 cm^–1 (C–C and C–N stretching of proteins and carotenoids), 1245 cm^–1 (amide III), 1451 cm^–1 CH₂ deformation (lipids and proteins), 1520 cm^–1 (β-carotene; CC stretching mode), 1665 cm^–1 (lipids (CC stretching)), 2852 cm^–1 (symmetric CH₂ stretching of lipids), 2889 cm^–1 (asymmetric CH₂ stretching of lipids), 2938 cm^–1 (C–H stretching, aromatic and aliphatic amino acids, charged amino acids, proline, threonine, histidine, lipids, proteins), and 3063 cm^–1 (nucleic acid to protein ratio; C–H aromatic ring vibration) were identified. , The exact peak positions of the average Raman spectra plotted in Figure A are summarized in Table S2. In this study, to achieve better classification with higher accuracy, we utilized a machine learning approach, partial least-squares discriminant analysis (PLS-DA), to analyze and visualize Raman spectra. PLS-DA is considered a supervised form of principal component analysis (PCA) while reducing the number of latent variables to a lower-dimensional space that minimizes error. Figure B presents the PLS-DA results, which revealed distinct clustering between TNBC and normal tissues. Figure C presents the PCA biplot, created by using the first and second principal components. In this plot, pink dots representing TNBC samples are predominantly clustered on the right side, while blue dots representing normal tissues are more concentrated on the left side. Together, the first two principal components account for 28.7% of the total variability. Additionally, arrows pointing to the right correspond to Raman peaks at 748, 755, 1206, 1517, and 1519 cm^–1, highlighting their stronger contribution to distinguishing TNBC data. The results show that there is a large heterogeneity among tumors collected from patients.

2.

Comparison of Raman spectra analysis for TNBC and matched breast tissues of all groups representing (A) average of Raman spectra, (B) partial least-squares discriminant analysis (PLS-DA) of the Raman data showing the distinct cluster for TNBC and matched breast tissues, (C) principal component analysis (PCA) biplot for the first and second principal components showing the important Raman peaks responsible for the separation, and (D) ratio comparison of Raman bands at 1660/1440 cm^–1. In the Raman spectra, the solid line shows the mean of the spectra, and the shaded area indicates the standard deviation.

The alterations in cancerous and matched breast tissues were further evaluated using PCA analysis of the Raman spectra collected from each tissue type. This involved calculating the percentage of variables in the Raman bands, reflecting changes in the content of components such as lipids and proteins because of cancer. For this purpose, Raman spectra of all replicates in the range of 100–3200 cm^–1 for both groups of cancerous and matched breast tissues from all three groups were categorized as the first and second principal components (PC1 and PC2). The second column of Figure S1A–C displays the PCA analysis of Raman spectra for each group. In all groups, the first PCs captured most of the variances within the data set, accounting for 92.8% in group 1, 83.6% in group 2, and 84.3% in group 3. The second PC, which distinguishes between matched breast tissue and cancerous groups, accounted for 3.5% in group 1, 7.2% in group 2, and 7.0% in group 3. Together, the first two PCs explained a total variance of 96.3% for group 1, 90.8% for group 2, and 91.3% for group 3. Peaks at around 1445 and 1660 cm^–1 were observed in both types of cancerous tissues and matched breast tissues of all three groups, which are associated with lipids. The intensity of the area under these peaks was measured and utilized to determine the level of lipid unsaturation by calculating the ratio of unsaturated lipid (CC at 1660 cm^–1) to saturated lipid (CH₂ at 1445 cm^–1) bands. The calculated ratio can be seen in Figure D, which indicates an increase in lipid unsaturation (P < 0.0001, Mann–Whitney two-tailed test) following breast cancer based on the mean ratio for all three groups. While the Raman spectra of cancerous and matched breast tissues appear similar, notable differences exist due to biochemical changes related to proteins, lipids, and other components. To explore these variations between cancerous and matched breast tissues, we employed analyses such as volcano plots, heat maps, and variable importance in projection (VIP) scores. The data was generated by analyzing the intensity of the Raman spectra, representing the expression levels of the biomolecular constituents within the tissues, as shown in Figure . The volcano plot in Figure A highlights the Raman peak positions that are upregulated (shown in red) and downregulated (shown in blue) in TNBC tissues compared with matched breast tissues. Specifically, the peaks at 748, 1517, and 1523 cm^–1 are upregulated, while the peaks at 2780, 2851, and 2881 cm^–1 are downregulated in TNBC tissues. Figure B illustrates the hierarchical heatmap clustering analysis, showing the upregulated peaks in red and the downregulated peaks in blue for each peak position. The results indicate that the peaks at 278, 748, 958 cm^–1 (carotenoid/cholesterol), 994, 1002 cm^–1 (phenylalanine/proteins), 1206 cm^–1 (tyrosine), 1155 cm^–1 (proteins/cholesterol), 1175 cm^–1 (tyrosine), 1517 cm^–1 (carotenoid), 1519 cm^–1 (β-carotene), 1523 cm^–1 (β-carotene), 1555 cm^–1 (proteins), 1558 cm^–1 (proteins), 1583, 1586, 1605 cm^–1 (tyrosine/phenylalanine), 1608 cm^–1 (tyrosine/phenylalanine), and 2332 cm^–1 are upregulated, whereas the peaks at 2849 cm^–1 (lipids), 2851 cm^–1 (lipids), 2877 cm^–1 (lipids), 2878 cm^–1 (lipids), 2881 cm^–1 (lipids), 2886, and 2812 cm^–1 are downregulated in TNBC tissues. The VIP score analysis (Figure C) highlights the distinct Raman peak positions between the TNBC and normal tissue samples for each group. Notably, the upregulated peaks at 278, 295, 748, 755, 923, 994, 1002, 1203, 1206, 1513, 1517, 1519, 1523, 1555, 1558, 1586, and 2332 cm^–1, along with the downregulated peaks at 388, 875 cm^–1, and 2780 (lipids) cm^–1, effectively differentiate between the two tissue types. Peaks associated with proteins at 1002, 1155, 1555, and 1558 cm^–1 are prominent in cancerous tissue, whereas Raman bands linked to lipids, such as 2849, 2851, 2877, 2878, and 2881 cm^–1, dominate in matched breast tissues. Figure D presents a violin plot comparing the mean intensities of the 2780 cm^–1 peak, associated with lipids, and the 1517 cm^–1 peak, linked to carotenoids. The results indicate a lower mean intensity for the lipid-associated peak at 2780 cm^–1 and a higher mean intensity for the carotenoid-associated peak at 1517 cm^–1 in TNBC tissues compared to that in normal tissues. The elevated β-carotene levels in TNBC compared to matched breast tissue may stem from tumor-driven metabolic reprogramming, including increased lipid uptake, storage, and altered retinoid metabolism. β-carotene serves as a precursor for retinoic acid, which regulates cell differentiation and proliferation. TNBC may have an altered retinoid metabolism, , leading to an accumulation of β-carotene due to inefficient conversion to retinoic acid. TNBC cells, under high oxidative stress, may accumulate β-carotene as an adaptive antioxidant response. − Additionally, enhanced lipid droplet formation and dysregulated lipid transport proteins could contribute to their sequestration within the tumor. These findings highlight a potential metabolic vulnerability in TNBC that can be leveraged for therapeutic targeting.

3.

Profiling Raman peak intensity changes in TNBC and normal tissues: (A) volcano plot illustrating significant upregulated and downregulated peaks, (B) clustered heatmap showcasing distinct Raman peak patterns between TNBC and normal tissues, (C) variable importance in projection (VIP) scores highlighting upregulated (red) and downregulated (blue) peaks, and (D) violin plot comparing the mean intensities of the lipid-associated peak at 2780 cm^–1 and the carotenoid-associated peak at 1517 cm^–1.

After applying Raman spectroscopy, which is an untargeted method, we employed Raman microscopy to target specific components and profile lipid and protein distribution within cancerous and matched breast tissues. To generate spatial mapping of the components and show their distribution in the tissues, we analyzed Raman images of the samples using the DCLS method and generated images of ω-3, ω-6, and ω-9 FAs, like α-LA, SDA, EPA, DPA, DHA, LA, AA, OA, and TG, and matrix proteins, like COL I, COL III, COL IV, COL V, FN, and LAM. An example of the generated Raman mapping image for both cancerous and matched breast tissues is shown in Figure A,B for COL I, LAM, and COL III for patients nos. 1, 2, and 3, respectively. Figure C quantitatively compares the protein score extracted from their corresponding Raman mapping images, in which there is a significant (p < 0.01) decrease in the level of COL I and COL III and a significant increase (p < 0.01) in the level of LAM within cancerous tissues in comparison to matched normal tissues. We analyzed comprehensive Raman mapping images for all fatty acid components to identify their distribution, and the generated images can be seen in Figures S2A,B and S3A,B for patient 1, Figures S5A,B and S6A,B for patient 2, and Figures S8A and S9A,B for patient 3. In these mapping images, red indicates a higher concentration of the components, while blue signifies a lower concentration of the same fatty acids. Moreover, Raman images of the samples were analyzed to generate spatial mapping images of different proteins such as COL I, COL III, COL IV, COL V, FN, and LAM, which can be seen in Figure S4A,B for patient 1, Figure S7A,B for patient 2, and Figure S10A,B for patient 3.

4.

H&E and Raman mapping images represent the distribution of COL I in group 1, LAM in group 2, and COL III in Group 3 within (A) TNBC and (B) matched breast tissues and (C) Raman score plot. Scale bars: 1 mm.

In addition to Raman mapping, which shows the distribution of components within tissues, we also performed DCLS analysis on the Raman spectra to quantitatively measure the lipid and protein changes in tissues. Figure A–H presents the mean of the lipid scores extracted by DCLS analysis of Raman spectra for all three patients for the respective fatty acids. To achieve this, Raman spectra collected from lipid standards of various FAs provided by Corning Life Sciences were used for DCLS analysis. Each patient’s sample spectrum was compared against specific lipid standardsα-LA, EPA, DHA, AA, and LAto generate a lipid score for each component. For further validation, we also performed LC–MS analysis, the result of which can be seen in Figure I–P.

5.

Representative bar charts showing comparison of lipids: α-LA, EPA, DHA, LA, and AA, total of ω-3, total of ω-6, and LA/α-LA measured by (A–H) Raman spectroscopy and (I–P) lipidomics methods for TNBC and matched breast tissues.

Based on the data, the level of all FAs (except α-LA) was elevated in the cancerous tissues compared with that in the matched breast tissues. For the α-LA FA, while LC–MS analysis showed a decreasing trend in cancer tissue in comparison to that in breast tissue, the Raman analysis indicated no changes between the two groups. After comparing each component, we calculated the levels of ω-3 (α-LA, EPA, and DHA) and ω-6 (AA and LA) FAs. According to these two methods, the cancerous tissues indicated higher levels for both ω-3 and ω-6 FAs. Moreover, due to the importance of LA and α-LA, which are the main components of ω-3 and ω-6 FA families, respectively, we calculated the ratio of LA/α-LA using both methods, revealing that a higher level of LA/α-LA was observed in cancer tissues compared to the matched breast tissues.

For further quantitative analysis of Raman data with proteins, we analyzed collected Raman spectra using the DCLS method to generate a specific protein score for each component within the Raman spectra. The protein scores generated through Raman spectroscopy and the DCLS method were plotted and are shown in Figure A–F. In addition to Raman microspectroscopy, several methods can be applied to examine the expression of proteins, including proteomics and immunohistochemistry (IHC), which is semiquantitative. In our study, to compare our Raman results for protein expression, we employed a proteomics technique, which is a destructive method and damages the structure of the tissue, and the results of the analysis are shown in Figure G–L. Our results showed that there is a positive correlation between the two methods. According to these results, TNBC tissues indicated lower expression levels of COL I, COL III, COL IV, and LAM and higher expression levels of FN and COL V compared with the breast tissue based on both Raman and proteomics techniques.

6.

Representative bar charts showing comparison of proteins: collagen I, collagen III, collagen IV, collagen V, fibronectin, and laminin were measured by (A–F) Raman and (G–L) proteomics methods for TNBC and matched breast tissues.

TNBC samples were compared with matched breast adipose tissue for differences in protein profiles. Results demonstrated that the matched breast adipose tissues were distinguished from TNBC by a multivariate analysis of proteomics data. The PC1 axis (explaining 38.3% variance) and PC2 axis (explaining 19.1% variance) revealed clear separation between the TNBC tumor and control groups, indicating significant proteomic differences between these groups, as shown in Figure A. Comparing TNBC tumor samples to matched breast tissue, the volcano plot analysis found that 463 total proteins significantly altered between the two groups, as shown in Figure B. Pathway analysis in Figure C shows enrichment of protein changes associated with focal adhesion, ECM–receptor interactions, and FA breakdown, which is in accordance with the observed changes in our Raman and LC–MS analyses. Analysis of Gene Ontology (GO) biological processes demonstrated significant changes in the ECM and structure organization, cellular detoxification, and chromatin assembly, as shown in Figure D. Significantly enhanced categories in terms of cellular components included blood microparticles, ECM including collagen, focal adhesion sites, and nucleosome structures, as shown in Figure E. ECM structural elements, antioxidant activity, protein heterodimerization activity, and lipase inhibitor activity were significantly changed by molecular function enrichment (Figure F). These results, taken together, show clear proteome reprogramming associated with the ECM and the extracellular TME in TNBC.

7.

Proteomic analysis and pathway and Gene Ontology analysis in TNBC. (A) PCA score plot clearly distinguishes between TNBC tumor and control samples based on proteomic profiles. (B) Volcano plot depicting differentially expressed proteins between TNBC tumor and control tissues (red indicates upregulation, blue indicates downregulation). (C) KEGG pathway enrichment analysis for differentially expressed proteins. (D–F) GO enrichment of biological processes (D), cellular components (E), and molecular functions (F). Enrichment scores are calculated using −log10 (p-value); the dot size represents the gene count, while color denotes p-value significance.

We built a protein–protein interaction network (Figure A) based on differentially expressed proteins to identify the important functional modules linked with TNBC. Three main connected clusters were identified by the network analysis: enzymes including alcohol dehydrogenases (ADH1A, ADH1B, and ADH5), acyl-CoA thioesterases (ACOT1, ACOT2), and hydroxyacyl-CoA dehydrogenase (HADHA, HADHB) are connected to lipid metabolism-related proteins (green nodes). Collagens (COL6A3, COL6A2), integrins (ITGAV, ITGA7), laminins (LAMA3, LAMB2), matrix adhesion proteins (SDC1, THBS2), and ECM-related proteins (pink nodes) constituted the largest cluster. The clustering of tumor-specific proteins (blue nodes), including poly­(ADP-ribose) polymerase 1 (PARP1) and vimentin (VIM), suggested roles as either targets or biomarkers unique to TNBC. The heatmap of the proteins in the interaction network is shown in Figure B. The heatmap of the top 75 differentially expressed proteins found in the study is shown in Figure C.

8.

Network analysis and mechanistic model of TNBC proteome. (A) Protein–protein network illustrating three primary protein categories: lipid metabolism proteins (green), extracellular matrix-related proteins (pink), and tumor-specific genes (blue). (B) Heatmap of the proteins in the protein–protein interaction network. (C) Heatmap illustrating the expression levels of the top 75 proteins in TNBC and control samples.

To better understand how the lipid profile is remodeled in TNBC, we summarized the data in a circos plot (Figure A). Lipid species were labeled by subclass and annotated with their total acyl chain length and degree of unsaturation. Within each subclass, species were organized from fully saturated (number of double bonds, n = 0) to highly polyunsaturated (n ≥ 3) and, within each group, ranked by increasing chain length. The TNBC lipidome showed a consistent shift toward polyunsaturated fatty acid (PUFA) species. Phosphatidylethanolamine (PE) and phosphatidylinositol (PI) contained highly enriched polyunsaturated lipids, such as PE (42:8), PI (42:9), and PI (44:12). Phosphatidylglycerol (PG) species like PG (38:7) also showed the same trend. In contrast, shorter and less unsaturated phosphatidylcholine (PC) and lysophosphatidylcholine (LPC) species, including PC (30:0), PC (32:0), and LPC (20:4), were reduced in TNBC compared to normal samples. The inner track of the circos plot, which reflects chain elongation, revealed a clear increase in the level of elongation among polyunsaturated PE and PI species. Together, these data indicate that TNBC tumors are characterized by a loss of short, saturated PCs and LPCs, alongside an enrichment of long-chain, highly unsaturated PEs, PIs, and PGs. This remodeling suggests metabolic reprogramming that promotes both elongation and desaturation, contributing to a lipid environment.

9.

(A) Circos plot illustrates lipidomic changes in TNBC tumors related to control samples. This circle plot visualizes tumor-associated changes in lipid abundance and fatty acyl chain characteristics across lipid species. The outer ring displays the log2-fold change in median-normalized lipid abundance in TNBC tumors versus control samples. Red bars pointing upward indicate upregulated lipids in tumors; bars pointing inward represent decreased levels. Asterisks (*) denote lipid species with significant fold-change (p < 0.05). The inner ring shows the fatty acyl elongation index for each lipid species, with bar colors indicating the degree of unsaturation: Light blue, saturated (n = 0); sky blue, monounsaturated (n = 1); steel blue, diunsaturated (n = 2); navy, polyunsaturated (n ≥ 3). Each sector is labeled with the lipid’s chain length and double bond count, and the lipid subclass (e.g., PC, PE) is shown at the inner edge. Subclass labels are color-coded. A central legend summarizes saturation color codes. (B) Quantification of total lipid class abundance (ng/mL) in TNBC tumors compared to control samples. TNBC tumors demonstrate increased concentrations of LPC, LPI, PC, PE, and PI species. Data are expressed as mean ± standard deviation for each category. (C) Pathway analysis of the lipids using BioPAN; the lipid type is shown by the node forms; glycerophospholipids and glycerolipids are represented by circles. Furthermore, a negative z-score is shown by purple lines, a positive z-score is shown by green lines, and a z-score linked to a lipid class or species change is indicated by shaded lines.

The BioPAN lipid pathway analysis (Figure C) indicated unique metabolic alterations in the TNBC samples. Reactions involving LPE → PE (−0.117) showed decreased PE biosynthesis, while suppressed reactions (PC → PS, −1.058; PI → LPI, −0.859; and LPC → PC, −0.584) showed decreased flux toward PS and PC synthesis pathways. These changes indicate considerable metabolic reprogramming, as evidenced by suppressed LPE to PE conversion and altered glycerophospholipid balance, which may support membrane remodeling and tumor growth.

The targeted lipidomic study showed that TNBC samples were significantly different from breast adipose control samples, as shown in Figure . The volcano plot in Figure A showed that some lipid species were significantly differentially expressed (P < 0.05). These included PI (40:7), PG (36:7), and PG (36:4) that were upregulated, and LPC (20:5) and LPC (20:4) that were downregulated. The OPLS-DA (Figure B) clearly separated the lipid profiles of TNBC and the control groups, showing strong metabolic differences. The main and orthogonal components accounted for 18.4 and 33.3% of the variance, respectively. Heatmap analysis in Figure C showed that some polyunsaturated PE and PI lipid species were found to be more abundant in TNBC. Individual lipid species abundances, as shown in Figure D with boxplots, confirmed these lipidomic changes, showing that polyunsaturated PI and PG lipids increased significantly, while LPC species decreased in TNBC samples.

10.

Lipidomics analysis in TNBC. (A) Volcano plot depicting differentially expressed lipids between TNBC tumor and control tissues (red indicates upregulation, blue indicates downregulation). (B) OPLS-DA score plot clearly distinguishes between TNBC tumor and control samples based on lipidomic profiles. (C) Heatmap illustrating the expression levels of lipid species in TNBC and control samples. (D) Bar plots showing individual lipid species with consistent upregulation or downregulation in TNBC tumor and control samples.

Discussion

Abnormalities within breast tissue are inherently heterogeneous in both morphology and molecular composition. − In this study, we applied histological images to demonstrate structural variations within cancerous and normal breast tissues. Additionally, variations in lipid and protein levels within the tissues were evaluated by using Raman microspectroscopy combined with lipidomic and proteomic analyses. In all techniques, tissues from both normal and cancerous regions of the breast were extracted and examined from three patients. Raman spectroscopy analysis revealed several significant differences, including variations in the intensity of bands associated with amino acids, nucleic acids, proteins, carbohydrates, and lipids. For example, the ratio analysis of lipid unsaturation over saturated lipid bands indicated an increase in lipid unsaturation in breast cancer tissues compared to normal tissues (Figure C).

Moreover, Raman spectra collected from whole tissue slides of normal and cancerous samples were analyzed by using the DCLS method to identify lipid and protein scores of each specific component within tissues. The results of lipid scores extracted from DCLS indicated no change in the level of α-LA and an increase in the levels of EPA, DHA, AA, and LA within cancerous tissues compared to normal tissues. To validate these results, we applied the lipidomics technique, which also demonstrated a positive correlation with our DCLS results of Raman spectra, except for α-LA, which showed a decreasing trend in cancer tissues compared to the matched breast adipose tissues. While both Raman and LC–MS/MS analyses revealed elevated levels of ω-3 and ω-6 fatty acids in cancer tissues compared to the normal ones based on the LC–MS/MS results, the difference of ω-6 is much higher (about 8767 (pmol/mg)) than the difference of ω-3 (about 788 (pmol/mg)) in cancer tissues than the matched normal tissues.

Lipid remodeling is an established metabolic cancer hallmark. TNBC cells take up exogenous FAs from the TME and can also switch to de novo FA synthesis as an alternative source of energy required for increased cell proliferation. ,, Metabolic profiling of TNBC shows that FA biosynthesis upregulates cellular pathways like the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway, a central cancer signaling pathway actively involved in cell proliferation and metabolism. −

Consistent with our findings, studies have shown an altered composition of PUFA in normal versus tumor tissues. , PUFAs also play a role in the lipid composition of the breast tissue TME, favoring tumor progression. You et al. showed the elevation of PUFA composition in TME of both proximal and distal tissues obtained from breast cancer patients with invasive ductal carcinoma and rat mammary tumor models compared to their matched normal. Elevated levels of lipids have also been reported in breast cancer cells. , Literature data on the lipid profiling of MDA-MB-231 (a TNBC cell line) also showed an increased expression of PUFA compared to MCF-7, a hormone receptor-positive breast cancer cell line.

However, the effects of ω-6 and ω-3 FAs are reported to be pro-oncogenic and antioncogenic, respectively, through the activation of different sets of metabolic genes. Therefore, these PUFAs are considered to have a pleiotropic impact on cancer pathology. For example, linoleic acid, a major ω-6 PUFA, promotes invasion and migration in MDA-MD-231 cells by activating the PI3K/AKT pathway through the induction of its upstream proteins, EGFR and PI3K. EPA and DHA, which are ω-3 PUFAs, have been shown to reduce cell proliferation in breast cancer cell lines and in vivo models, while ω-6 have been shown to promote cell growth. , ω-3 fatty acids also synergistically enhance the effect of chemotherapy. , PUFAs, therefore, provide a new rationale as a target for therapeutics or to be used as a tool to enhance existing chemotherapeutic regimens. ,

As a crucial element of tissues in multicellular organisms, the ECM consists of various proteins including collagens, laminins, fibronectin, glycoproteins, and proteoglycans, functioning as a structural framework, offering the support required to preserve tissue integrity. The results of protein score data extracted from DCLS data for proteins showed lower expression of COL I, COL III, COL IV, and LAM and higher expression of COL V and FN, compared to the normal tissue, which aligns with the results of proteomics. The basement membrane acts as a barrier and protects the tissue from the initial invasion of tumor cells. We and others have previously highlighted the loss of COL I and COL III mRNA in TNBC compared to matched breast adipose. Lochter et al. reported decreased expression of COL IV and LAM and increased expression of COL V and FN in carcinoma cells and breast tumors. Moreover, in some invasive breast carcinomas, a reduction in the level of COL IV was detected, indicating basement membrane degradation during breast cancer. ,

Our comprehensive proteomic and lipidomic study demonstrates significant changes in TNBC samples relative to those of matched breast adipose samples. Proteomic data revealed substantial changes in proteins associated with lipid metabolism and ECM remodeling (Figures A–F and A–C). Additionally, the volcano plot in Figure B revealed several differentially expressed proteins, including upregulated ATIC and MYO1D and downregulated ADH1A and CAPNS2. These proteins are linked to key biological processes: ATIC with metabolic reprogramming, MYO1D with cell migration, ADH1A with angiogenesis and tumor suppression, and CAPNS2 with the formation of invadopodiaspecialized structures that help cells degrade the ECM during migration and invasion. Pathway and network analyses validate that ECM remodeling and lipid metabolism are essential interrelated processes supporting tumor growth. These data align with research highlighting ECM modifications important to cancer metastasis and lipid metabolism as a defining characteristic of cancer cell proliferation and survival. ,

The results were further confirmed by the lipidomic studies, which showed that there were significant changes toward PUFAs and an extension of fatty acyl chains in the PI, PE, and PG lipid subclasses (Figure A). According to Baenke et al. and Röhrig and Schulze, BioPAN analysis found that there was a decrease in PE biosynthesis (LPE → PE) and also a decrease in PC to PS conversion pathways (Figure C). This suggests that there is a change in glycerophospholipid homeostasis, which is likely to play a role in the membrane remodeling necessary for tumor growth. Figure A–D shows that the changes in lipidomics were confirmed at the species level, with an accumulation of polyunsaturated PI, LPC, and PC lipids. Altogether, our findings provide support to the idea that TNBC’s aggressive phenotype and metabolic adaptability are made possible by coordinated proteome and lipidomic reprogramming, and they point to possible treatment targets in pathways related to lipid metabolism and ECM remodeling.

Conclusions

We utilized Raman spectroscopy as a nondestructive and spatially resolved analytical platform to map unsaturated fatty acids and extracellular matrix (ECM) proteins in tumor microenvironments. We validated these findings through quantitative liquid chromatography–mass spectrometry (LC–MS) and proteomic analysis. We demonstrate that TNBC tumors exhibit elevated levels of polyunsaturated fatty acids (EPA, DHA, AA, LA, ω-3/ω-6) and remodeling of ECM components. These findings are supported by direct classical least-squares (DCLS) modeling of Raman spectra and confirmed with biochemical quantification. Furthermore, pathway and network analyses built using the proteomics results revealed coordinated reprogramming of lipid metabolism and ECM remodeling, key hallmarks of TNBC aggressiveness. This comprehensive molecular mapping underscores the utility of Raman-based spatial lipidomics and proteomics as powerful, minimally invasive tools for studying cancer heterogeneity and tissue microenvironments.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/cbmi.5c00104.

• LC–MS mass spectrometry methodology for proteomics, LC–MS/MS analysis of phospholipids and fatty acids, table showing the Raman peak assignments for normal and TNBC tissue, Raman spectra and PCA results for TNBC and matched normal tissues for all patients, and Raman mapping images for TNBC and matched normal tissues showing the spatial distribution of lipids (ω-3, ω-6, and ω-9 fatty acids, triacylglycerol (TG)) and proteins (collagen I, III, IV, V, fibronectin, laminin) (PDF)

The authors declare no competing financial interest.