[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124177-en":3,"doc-seo-124177-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124177,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Cancer Cell Line Classification Using Raman Spectroscopy of Cancer-Derived Exosomes and Machine Learning","Liquid biopsies are an emerging, noninvasive tool for cancer diagnostics, utilizing biological fluids for molecular profiling. Nevertheless, current methods often lack the sensitivity and specificity required for early detection and real-time monitoring. This work applies machine learning to Raman spectra to classify exosome solutions by cancer origin and analyzes lipid differences across exosomes. Using PCA for feature extraction and linear discriminant analysis for prediction, the PCA-LDA framework reaches 93.3% overall accuracy and high F1 scores, supporting lipid dynamics and exosome functions for early diagnostic development and model refinement.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nCancer Cell Line Classification Using Raman Spectroscopy of Cancer-Derived Exosomesand Machine Learning.  \nPermalink  \n[https://escholarship.org/uc/item/46g9v6jj](https://escholarship.org/uc/item/46g9v6jj)  \nJournal  \nAnalytical Chemistry, 97(13)  \nAuthors  \nVillazon, Jorge  \nDela Cruz, Nathaniel Shi, Lingyan  \nPublication Date  \n2025-04-08  \nDOI  \n10.1021/acs.analchem.4c06966  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nThis article is licensed under CC-BY-NC-ND 4.0  \n[pubs.acs.org/ac](pubs.acs.org/ac)  Article   \nCancer Cell Line Classification Using Raman Spectroscopy of CancerDerived Exosomes and Machine Learning  \nJorge Villazon, Nathaniel Dela Cruz, and Lingyan Shi *  \n Cite This: Anal. Chem. 2025, 97, 7289−7298  \nRead Online  \n\n|  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |  | *sı Supporting Information |\n\nABSTRACT: Liquid biopsies are an emerging, noninvasive tool for cancer diagnostics, utilizing biological fluids for molecular profiling. Nevertheless, the current methods often lack the sensitivity and specificity necessary for early detection and real-time monitoring. This work explores an advanced approach to improving liquid biopsy techniques through machine learning analysis of the Raman spectra measured to classify distinct exosome solutions by their cancer origin. This was accomplished by conducting principal component analysis (PCA) of the Raman spectra of exosomes from three cancer cell lines (COLO205, A375, and LNCaP) to extract chemically significant features. This reduced set of features was then utilized to train a linear discriminant analysis (LDA) classifier to predict the source of the exosomes. Furthermore, we investigated differences in the lipid composition in these exosomes by their spectra. This spectral similarity analysis revealed differences in lipid profiles between the different cancer cell lines as well as identified the predominant lipids across all exosomes. Our PCA-LDA framework achieved 93.3% overall accuracy and F1 scores of 98.2%, 91.1%, and 91.0% for COLO205, A375, and LNCaP, respectively. Our results from spectral similarity analysis were also shown to support previous findings of lipid dynamics due to cancer pathology and pertaining to exosome function and structure. These findings underscore the benefits of enhancing Raman spectroscopy analysis with machine learning, laying the groundwork for the development of early noninvasive cancer diagnostics and personalized treatment strategies. This work potentially establishes the foundation for refining the classification model and optimizing exosome extraction and detection from clinical samples for clinical translation.  \n■ INTRODUCTION  \nEarly diagnosis significantly improves the likelihood of successful outcomes of cancer treatments such as radiation and surgery. Despite many improvements in diagnostic technology, nearly half of all cancer cases are still identified only at an advanced stage. 1−4 Current screening methods like imaging and biopsies have significant drawbacks as they are often expensive, labor-intensive, and invasive. Moreover, they provide limited molecular information for precise characterization and staging that limits their use in early stage diagnosis.5,6 Consequently, there is growing interest in developing noninvasive cancer diagnostics that can detect the subtle molecular changes associated with early cancer development.  \nLiquid biopsies present a noninvasive alternative to traditional biopsies by analyzing cancer-specific biomarkers in bodily fl","cbCaic2paKknxI8Z","https://ap.wps.com/l/cbCaic2paKknxI8Z","pdf",4063804,1,11,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"How does the study use machine learning with Raman spectra for cancer diagnostics?\",\"answer\":\"The work performs PCA on Raman spectra to extract chemically significant features, then trains a linear discriminant analysis classifier to predict the exosome source cancer cell line. This links spectral patterns to cancer origin for diagnostic classification.\"},{\"question\":\"Which cancer cell lines and exosome samples are used in the analysis?\",\"answer\":\"Exosomes are derived from three cancer cell lines: COLO205, A375, and LNCaP. The Raman spectra of these exosome solutions are used for PCA feature extraction and classifier training.\"},{\"question\":\"What performance did the PCA-LDA framework achieve?\",\"answer\":\"The framework achieved 93.3% overall accuracy. Reported F1 scores are 98.2% for COLO205, 91.1% for A375, and 91.0% for LNCaP.\"}]","Cancer Cell Line Classification Using Raman Spectroscopy of Cancer-Derived Exosomes and Machine Learning | PDF",1785820872,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cancer-cell-line-classification-using-raman-spectroscopy-of-cancer-derived-exosomes-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cancer-cell-line-classification-using-raman-spectroscopy-of-cancer-derived-exosomes-and-machine-learning/124177/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study use machine learning with Raman spectra for cancer diagnostics?","Question",{"text":75,"@type":76},"The work performs PCA on Raman spectra to extract chemically significant features, then trains a linear discriminant analysis classifier to predict the exosome source cancer cell line. This links spectral patterns to cancer origin for diagnostic classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which cancer cell lines and exosome samples are used in the analysis?",{"text":80,"@type":76},"Exosomes are derived from three cancer cell lines: COLO205, A375, and LNCaP. The Raman spectra of these exosome solutions are used for PCA feature extraction and classifier training.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance did the PCA-LDA framework achieve?",{"text":84,"@type":76},"The framework achieved 93.3% overall accuracy. Reported F1 scores are 98.2% for COLO205, 91.1% for A375, and 91.0% for LNCaP.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]