{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T05:10:37Z","timestamp":1789017037245,"version":"build-2803163510"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2021YFF0502900"],"award-info":[{"award-number":["2021YFF0502900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62527809"],"award-info":[{"award-number":["62527809"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62225505"],"award-info":[{"award-number":["62225505"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62675224"],"award-info":[{"award-number":["62675224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["62527809"],"award-info":[{"award-number":["62527809"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62225505"],"award-info":[{"award-number":["62225505"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62675224"],"award-info":[{"award-number":["62675224"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"National Key Research and Shenzhen Key Projects","award":["JCYJ20200109105404067"],"award-info":[{"award-number":["JCYJ20200109105404067"]}]},{"name":"Shenzhen Talent Innovation Project","award":["RCJC20210706091949022"],"award-info":[{"award-number":["RCJC20210706091949022"]}]},{"name":"ZTE Industry\u2013University\u2013Institute Cooperation Funds","award":["IA20241202008"],"award-info":[{"award-number":["IA20241202008"]}]},{"name":"Shenzhen Key Laboratory of Photonics and Biophotonics","award":["ZDSYS20210623092006020"],"award-info":[{"award-number":["ZDSYS20210623092006020"]}]},{"name":"Biomedical Optics Micro-Nano Detection and Imaging","award":["ZDSY20130329101226215"],"award-info":[{"award-number":["ZDSY20130329101226215"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Sensitive detection of treatment-associated Raman spectral alterations in tumor tissues remains challenging, particularly when such changes are not readily apparent from conventional morphological evaluation. Here, we developed a label-free Raman biosensing strategy combined with explainable machine learning to characterise treatment-associated spectral signatures in melanoma tumours. A B16-F10 melanoma-bearing mouse model was used to compare untreated and PBS-treated controls with cohorts receiving immune checkpoint blockade, anti-angiogenic intervention, or combination therapy. Raman spectra were acquired from multiple spatial regions of melanoma tissues and analyzed using nonlinear dimensionality reduction, supervised classification, and SHAP-based feature interpretation. Although cohort-averaged spectra showed substantial overlap, multivariate analysis revealed treatment-dependent spectral organization, with the combination-treatment cohort showing the most compact and distinguishable spectral profile. Supervised models, including convolutional neural networks, support vector machines, and k-nearest neighbors, further supported the reproducibility of treatment-associated Raman signatures when evaluated using mouse-level validation strategies. SHAP analysis identified discriminative Raman features mainly located within lipid, phospholipid, ester, protein, and collagen-associated vibrational domains, suggesting potential contributions from metabolic- and extracellular-matrix-related biochemical components to treatment-associated spectral discrimination. These findings indicate that Raman spectroscopy integrated with explainable machine learning provides a sensitive, label-free method for distinguishing treatment-associated spectral differences among melanoma tissues. The proposed approach may serve as a complementary spectroscopic tool alongside conventional histological and molecular analyses for investigating treatment-associated tissue-state alterations.<\/jats:p>","DOI":"10.3390\/bios16090501","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T11:23:51Z","timestamp":1788866631000},"page":"501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable AI-Assisted Label-Free Raman Biosensing Reveals Therapy-Associated Spectral Signatures in Melanoma Tumors"],"prefix":"10.3390","volume":"16","author":[{"given":"Muhammad Nouman","family":"Khan","sequence":"first","affiliation":[{"name":"Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingsong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqing","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asif","family":"Khalid","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"ref_1","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1001\/jamadermatol.2022.0160","article-title":"Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040","volume":"158","author":"Arnold","year":"2022","journal-title":"JAMA Dermatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108226","DOI":"10.1016\/j.envint.2023.108226","article-title":"Global, regional and national burdens of non-melanoma skin cancer attributable to occupational exposure to solar ultraviolet radiation for 183 countries, 2000\u20132019: A systematic analysis from the WHO\/ILO Joint Estimates of the Work-related Burden of Disease and Injury","volume":"181","author":"Pega","year":"2023","journal-title":"Environ. Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1001\/jamadermatol.2020.4616","article-title":"UV Exposure and the Risk of Cutaneous Melanoma in Skin of Color: A Systematic Review","volume":"157","author":"Lopes","year":"2021","journal-title":"JAMA Dermatol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"114074","DOI":"10.1016\/j.ejca.2024.114074","article-title":"Skin cancers are the most frequent cancers in fair-skinned populations, but we can prevent them","volume":"204","author":"Garbe","year":"2024","journal-title":"Eur. J. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.soc.2014.12.001","article-title":"Current Staging and Prognostic Factors in Melanoma","volume":"24","author":"Bartlett","year":"2015","journal-title":"Surg. Oncol. Clin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.it.2016.04.010","article-title":"Targeted Therapy and Checkpoint Immunotherapy Combinations for the Treatment of Cancer","volume":"37","author":"Hughes","year":"2016","journal-title":"Trends Immunol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1158\/2159-8290.CD-22-1427","article-title":"Late-Stage Metastatic Melanoma Emerges through a Diversity of Evolutionary Pathways","volume":"13","author":"Spain","year":"2023","journal-title":"Cancer Discov."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/s41377-023-01104-7","article-title":"Deep learning-enabled virtual histological staining of biological samples","volume":"12","author":"Bai","year":"2023","journal-title":"Light Sci. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.jaad.2018.07.072","article-title":"Usefulness of dermoscopy to improve the clinical and histopathologic diagnosis of skin cancers","volume":"80","author":"Braun","year":"2019","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1373\/clinchem.2019.303644","article-title":"Biomarkers for Predicting Response to Immunotherapy with Immune Checkpoint Inhibitors in Cancer Patients","volume":"65","author":"Duffy","year":"2019","journal-title":"Clin. Chem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4938","DOI":"10.1158\/1078-0432.CCR-16-1821","article-title":"PD-L1 Expression in Melanoma: A Quantitative Immunohistochemical Antibody Comparison","volume":"23","author":"Sunshine","year":"2017","journal-title":"Clin. Cancer Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e542","DOI":"10.1016\/S1470-2045(16)30406-5","article-title":"Predictive biomarkers for checkpoint inhibitor-based immunotherapy","volume":"17","author":"Gibney","year":"2016","journal-title":"Lancet Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.autcon.2017.11.002","article-title":"A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory","volume":"86","author":"Ding","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fattore, L., Ruggiero, C.F., Liguoro, D., Castaldo, V., Catizone, A., Ciliberto, G., and Mancini, R. (2021). The Promise of Liquid Biopsy to Predict Response to Immunotherapy in Metastatic Melanoma. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.645069"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1186\/s12943-018-0864-3","article-title":"Biomarkers for predicting efficacy of PD-1\/PD-L1 inhibitors","volume":"17","author":"Yi","year":"2018","journal-title":"Mol. Cancer"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s00259-017-3691-7","article-title":"The Advantages and Challenges of Using FDG PET\/CT for Response Assessment in Melanoma in the Era of Targeted Agents and Immunotherapy","volume":"44","author":"Wong","year":"2017","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11149","DOI":"10.1002\/cam4.5698","article-title":"Role of tumor microenvironment in cancer progression and therapeutic strategy","volume":"12","author":"Wang","year":"2023","journal-title":"Cancer Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.semcancer.2022.02.010","article-title":"Metabolic modulation of immune checkpoints and novel therapeutic strategies in cancer","volume":"86","author":"Wang","year":"2022","journal-title":"Semin. Cancer Biol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.ccell.2023.01.009","article-title":"Metabolic programming and immune suppression in the tumor microenvironment","volume":"41","author":"Arner","year":"2023","journal-title":"Cancer Cell"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1158\/2326-6066.CIR-19-0702","article-title":"Fatty Acid Oxidation Controls CD8+ Tissue-Resident Memory T-cell Survival in Gastric Adenocarcinoma","volume":"8","author":"Lin","year":"2020","journal-title":"Cancer Immunol. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.cmet.2019.11.021","article-title":"Mitochondrial Integrity Regulated by Lipid Metabolism Is a Cell-Intrinsic Checkpoint for Treg Suppressive Function","volume":"31","author":"Field","year":"2020","journal-title":"Cell Metab."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1038\/s41590-018-0251-7","article-title":"Metabolic reprogramming of natural killer cells in obesity limits antitumor responses","volume":"19","author":"Michelet","year":"2018","journal-title":"Nat. Immunol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1364\/AOP.479884","article-title":"Advances in Raman spectroscopy and imaging for biomedical research","volume":"15","author":"Siddhanta","year":"2023","journal-title":"Adv. Opt. Photon."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5020","DOI":"10.1021\/ac902697q","article-title":"Assessing Differentiation Status of Human Embryonic Stem Cells Noninvasively Using Raman Microspectroscopy","volume":"82","author":"Schulze","year":"2010","journal-title":"Anal. Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1007\/s00216-016-0065-0","article-title":"Monitoring doxorubicin cellular uptake and trafficking using in vitro Raman microspectroscopy: Short and long time exposure effects on lung cancer cell lines","volume":"409","author":"Farhane","year":"2017","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s00604-026-08126-z","article-title":"Serum KIM-1 Molecular Early Warning Radar: SERS Combined with Artificial Intelligence for Accurate Early Diagnosis of Chronic Kidney Disease","volume":"193","author":"Yu","year":"2026","journal-title":"Microchim. Acta"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"150077","DOI":"10.1016\/j.foodchem.2026.150077","article-title":"Magnetic Nanozymes for Dual-Modal SERS-Colorimetric Detection of Hydrogen Peroxide in Foods","volume":"522","author":"Li","year":"2026","journal-title":"Food Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1038\/ni.2956","article-title":"Cell-intrinsic lysosomal lipolysis is essential for alternative activation of macrophages","volume":"15","author":"Huang","year":"2014","journal-title":"Nat. Immunol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s00262-017-2003-1","article-title":"Tumor-associated fibrosis as a regulator of tumor immunity and response to immunotherapy","volume":"66","author":"Jiang","year":"2017","journal-title":"Cancer Immunol. Immunother."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1038\/nm.4123","article-title":"Targeting focal adhesion kinase renders pancreatic cancers responsive to checkpoint immunotherapy","volume":"22","author":"Jiang","year":"2016","journal-title":"Nat. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4558","DOI":"10.1073\/pnas.1815515116","article-title":"Blocking CXCR4 alleviates desmoplasia, increases T-lymphocyte infiltration, and improves immunotherapy in metastatic breast cancer","volume":"116","author":"Chen","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Noman, M.Z., Hasmim, M., Lequeux, A., Xiao, M., Duhem, C., Chouaib, S., Berchem, G., and Janji, B. (2019). Improving Cancer Immunotherapy by Targeting the Hypoxic Tumor Microenvironment: New Opportunities and Challenges. Cells, 8.","DOI":"10.3390\/cells8091083"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1007\/s00604-026-07862-6","article-title":"Immunochromatographic SERS Sensor-Deep Learning Combined Strategy for Intelligent Diagnosis of Chronic Kidney Disease","volume":"193","author":"Yu","year":"2026","journal-title":"Microchim. Acta"}],"container-title":["Biosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/9\/501\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T04:29:57Z","timestamp":1789014597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/9\/501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,8]]},"references-count":34,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["bios16090501"],"URL":"https:\/\/doi.org\/10.3390\/bios16090501","relation":{},"ISSN":["2079-6374"],"issn-type":[{"value":"2079-6374","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,8]]}}}