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Machine-learning analysis of serum proteomics in neuropathic pain after nerve injury in breast cancer surgery points at chemokine signaling via SIRT2 regulation

  • Background: Persistent postsurgical neuropathic pain (PPSNP) can occur after intraoperative damage to somatosensory nerves, with a prevalence of 29–57% in breast cancer surgery. Proteomics is an active research field in neuropathic pain and the first results support its utility for establishing diagnoses or finding therapy strategies. Methods: 57 women (30 non-PPSNP/27 PPSNP) who had experienced a surgeon-verified intercostobrachial nerve injury during breast cancer surgery, were examined for patterns in 74 serum proteomic markers that allowed discrimination between subgroups with or without PPSNP. Serum samples were obtained both before and after surgery. Results: Unsupervised data analyses, including principal component analysis and self-organizing maps of artificial neurons, revealed patterns that supported a data structure consistent with pain-related subgroup (non-PPSPN vs. PPSNP) separation. Subsequent supervised machine learning-based analyses revealed 19 proteins (CD244, SIRT2, CCL28, CXCL9, CCL20, CCL3, IL.10RA, MCP.1, TRAIL, CCL25, IL10, uPA, CCL4, DNER, STAMPB, CCL23, CST5, CCL11, FGF.23) that were informative for subgroup separation. In cross-validated training and testing of six different machine-learned algorithms, subgroup assignment was significantly better than chance, whereas this was not possible when training the algorithms with randomly permuted data or with the protein markers not selected. In particular, sirtuin 2 emerged as a key protein, presenting both before and after breast cancer treatments in the PPSNP compared with the non-PPSNP subgroup. Conclusions: The identified proteins play important roles in immune processes such as cell migration, chemotaxis, and cytokine-signaling. They also have considerable overlap with currently known targets of approved or investigational drugs. Taken together, several lines of unsupervised and supervised analyses pointed to structures in serum proteomics data, obtained before and after breast cancer surgery, that relate to neuroinflammatory processes associated with the development of neuropathic pain after an intraoperative nerve lesion.

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Metadaten
Verfasserangaben:Jörn LötschORCiDGND, Laura MustonenORCiDGND, Hanna HarnoORCiD, Eija KalsoORCiDGND
URN:urn:nbn:de:hebis:30:3-755779
DOI:https://doi.org/10.3390/ijms23073488
ISSN:1422-0067
Titel des übergeordneten Werkes (Englisch):International Journal of Molecular Sciences
Verlag:MDPI
Verlagsort:Basel
Dokumentart:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Veröffentlichung (online):23.03.2022
Datum der Erstveröffentlichung:23.03.2022
Veröffentlichende Institution:Universitätsbibliothek Johann Christian Senckenberg
Datum der Freischaltung:11.09.2023
Freies Schlagwort / Tag:data science; human research; machine-learning; neuropathic pain; pain; patients; postoperative pain; proteomics
Jahrgang:23
Ausgabe / Heft:7, art. 3488
Aufsatznummer:3488
Seitenzahl:29
HeBIS-PPN:513653465
Institute:Medizin
DDC-Klassifikation:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Lizenz (Deutsch):License LogoCreative Commons - Namensnennung 4.0