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Conventional histopathology, while widely used, remains invasive and subjective, restricting early-stage diagnosis. This study integrates confocal Raman spectroscopy with metabolomics to characterize biochemical and morphological features across normal, fibroadenoma, DCIS, and IDC tissues.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/integration-of-raman-spectroscopy-and-metabolomics-for-early-breast-cancer-detection-and-classification/345538/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/integration-of-raman-spectroscopy-and-metabolomics-for-early-breast-cancer-detection-and-classification/345538.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early breast cancer detection important in this study?","Question",{"text":112,"@type":113},"Breast cancer progression and outcomes depend on pathological characteristics, and early detection supports improved clinical results. 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China | 2Department of Pharmacy, Harbin Medical\u003Cbr>University, Harbin, Heilongjiang, P.R. China | 3Department of Gastroenterology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China Correspondence: Yang Li ([liy@hrbmu.edu.cn](liy@hrbmu.edu.cn)) | Fangjie Hu ([18846419361@163.com](18846419361@163.com))\u003Cbr>Received: 7 October 2025 | Revised: 7 April 2026 | Accepted: 13 April 2026\u003Cbr>Keywords: biochemical composition | breast tumors | metabolites | pathological states | Raman spectroscopy | tissue imaging |  |\n| --- | --- |\n| ABSTRACT\u003Cbr>Breast cancer, now the fourth leading cause of cancer-related mortality worldwide, necessitates early detection for improved clinical outcomes. Conventional histopathology, though widely used, is invasive and subjective, limiting its utility in early-stage diagnosis. Here, we integrated confocal Raman spectroscopy with metabolomics to analyze biochemical and morphological features of breast tissues, including normal, fibroadenoma, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) . Using spectral analysis and spectral unmixing, we mapped key biochemical components—proteins, lipids, and nucleic acids—across distinct tissue types. Cancerous tissues displayed heightened signals for proteins and nucleic acids but reduced lipids and carotenoids, reflecting profound metabolic alterations. Validation through metabolomic profiling revealed upregulated glycolytic and lipid synthesis pathways in tumor regions. Raman imaging further enabled precise classification of breast cancer subtypes, such as mucinous carcinoma and phyllodes tumors. These findings underscore the potential of Raman spectroscopy asa non-invasive diagnostic modality for early detection and classification of breast cancer. The integration of Raman imaging with machine learning presents a promising avenue for advancing precision oncology. |  |\n| 1 | Introduction | tissue specimens and conducting detailed analyses to ascertain the type, location, and degree of lesion invasion. However, these |\n| Breast cancer remains the most prevalent malignancy among | conventional methods are invasive, limited in scope, and sub- |\n| women globally, with its incidence rising annually, posing a | ject to observer bias. Moreover, traditional histological staining |\n| significant public health challenge [1, 2] . In-depth research on | methods, which focus on morphological analysis, are inade- |\n| early-stage breast cancer is academically crucial, as it holds the | quate for detecting lesions during the latent stages ofthe disease |\n| potential to enhance patient treatment outcomes and develop | and fail to provide critical biochemical information about the |\n| effective strategies for preventing recurrence and metastasis [3, 4] . The pathological progression of breast cancer is notably | tumor [5–7] . |\n| complex, with the prognosis and treatment intricately linked | As cancer progresses, tissue biochemical components undergo |\n| to the pathological characteristics of the lesions [5] . Current di- | significant changes that reflect tumor development, under- |\n| agnostic and pathological grading approaches predominantly | scoring the importance of early detection and intervention |\n| depend on histological examination, which involves obtaining | Traditional methods often fail to capture the dynamic evolution |\n| Abbreviations: CA, cancer; DCIS, ductal carcinoma in situ; FAT, fibroadenoma tissue; IDC, invasive ductal carcinoma; LDA, linear discriminant analysis; MBC, mucinous breast carcinoma; MMBC, mixed mucinous breast carcinoma; NBT, normal breast tissue; NMF, non-negative matrix factorization; PCA, pr","cbCaii3k89OI8aUf","https://ap.wps.com/l/cbCaii3k89OI8aUf","pdf",6078877,14,"English","# Abstract\n## Introduction\n## Methods and Results\n## Raman imaging and classification\n## Machine learning for precision oncology","[{\"question\":\"Why is early breast cancer detection important in this study?\",\"answer\":\"Breast cancer progression and outcomes depend on pathological characteristics, and early detection supports improved clinical results. Conventional histopathology is invasive and subjective, motivating non-invasive approaches.\"},{\"question\":\"What does the integrated approach combine?\",\"answer\":\"The work integrates confocal Raman spectroscopy with metabolomics, linking spectral and biochemical analyses to tissue biochemical and morphological features.\"},{\"question\":\"How were tissue biochemical components identified and compared across tumor types?\",\"answer\":\"Spectral analysis and spectral unmixing mapped proteins, lipids, and nucleic acids across normal, fibroadenoma, DCIS, and IDC tissues, showing proteins and nucleic acids increased while lipids and carotenoids decreased in cancerous tissue.\"}]","Integration of Raman Spectroscopy and Metabolomics for Early Breast Cancer Detection and Classification | PDF",1790058035,35]