Integrative Raman Spectroscopy and Multi-Omics Analysis of Lipid and Matrix Protein Heterogeneity in Triple-Negative Breast Cancer
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".