{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:34:47Z","timestamp":1771335287543,"version":"3.50.1"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003803","name":"University of Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003803","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:p>The prevalence rate of depression is higher in patients with fibromyalgia syndrome, but this is often unrecognized in patients with chronic pain. Given that depression is a common major barrier in the management of patients with fibromyalgia syndrome, an objective tool that reliably predicts depression in patients with fibromyalgia syndrome could significantly enhance the diagnostic accuracy. Since pain and depression can cause each other and worsen each other, we wonder if pain-related genes can be used to differentiate between those with major depression from those without. This study developed a support vector machine model combined with principal component analysis to differentiate major depression in fibromyalgia syndrome patients using a microarray dataset, including 25 fibromyalgia syndrome patients with major depression, and 36 patients without major depression. Gene co-expression analysis was used to select gene features to construct support vector machine model. The principal component analysis can help reduce the number of data dimensions without much loss of information, and identify patterns in data easily. The 61 samples available in the database were not enough for learning based methods and cannot represent every possible variation of each patient. To address this issue, we adopted Gaussian noise to generate a large amount of simulated data for training and testing of the model. The ability of support vector machine model to differentiate major depression using microarray data was measured as accuracy. Different structural co-expression patterns were identified for 114 genes involved in pain signaling pathway by two-sample KS test (<jats:italic>p<\/jats:italic> &amp;lt; 0.001 for the maximum deviation D = 0.11 &amp;gt; D<jats:sub><jats:italic>critical<\/jats:italic><\/jats:sub> = 0.05), indicating the aberrant co-expression patterns in fibromyalgia syndrome patients. Twenty hub gene features were further selected based on co-expression analysis to construct the model. The principal component analysis reduced the dimension of the training samples from 20 to 16, since 16 components were needed to retain more than 90% of the original variance. The support vector machine model was able to differentiate between those with major depression from those without in fibromyalgia syndrome patients with an average accuracy of 93.22% based on the expression levels of the selected hub gene features. These findings would contribute key information that can be used to develop a clinical decision-making tool for the data-driven, personalized optimization of diagnosing depression in patients with fibromyalgia syndrome.<\/jats:p>","DOI":"10.3389\/fgene.2023.1026672","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T05:27:01Z","timestamp":1680067621000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Use of pain-related gene features to predict depression by support vector machine model in patients with fibromyalgia"],"prefix":"10.3389","volume":"14","author":[{"given":"Fengfeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi Wai","family":"Cheung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stanley Sau Ching","family":"Wong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"B1","unstructured":"SVM classification with linear and RBF kernels\n            Apostolidis-AfentoulisV.\n            LioufiK.-I.\n          2015"},{"key":"B2","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.pec.2008.06.005","article-title":"Patient perspectives on the impact of fibromyalgia","volume":"73","author":"Arnold","year":"2008","journal-title":"Patient Educ. Couns."},{"key":"B3","doi-asserted-by":"publisher","first-page":"2433","DOI":"10.1001\/archinte.163.20.2433","article-title":"Depression and pain comorbidity: A literature review","volume":"163","author":"Bair","year":"2003","journal-title":"Archives Intern. Med."},{"key":"B4","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/0968-0004(92)90002-q","article-title":"Receptor classes and the transmitter-gated ion channels","volume":"17","author":"Barnard","year":"1992","journal-title":"Trends Biochem. Sci."},{"key":"B5","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/1471-2474-8-27","article-title":"An internet survey of 2,596 people with fibromyalgia","volume":"8","author":"Bennett","year":"2007","journal-title":"BMC Musculoskelet. Disord."},{"key":"B6","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1093\/bioinformatics\/btp101","article-title":"ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks","volume":"25","author":"Bindea","year":"2009","journal-title":"Bioinformatics"},{"key":"B7","doi-asserted-by":"publisher","first-page":"10973","DOI":"10.1038\/srep10973","article-title":"Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia","volume":"5","author":"Chan","year":"2015","journal-title":"Sci. Rep."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"Libsvm: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. intelligent Syst. Technol. (TIST)"},{"key":"B9","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/j.jpain.2015.06.004","article-title":"Bidirectional association between depression and fibromyalgia syndrome: A nationwide longitudinal study","volume":"16","author":"Chang","year":"2015","journal-title":"J. Pain"},{"key":"B10","first-page":"907","article-title":"The science of fibromyalgia","volume-title":"Mayo clinic proceedings","author":"Clauw","year":"2011"},{"key":"B11","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/bf00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"B12","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1176\/appi.ajp.2020.20050653","article-title":"Impact of the KCNQ2\/3 channel opener ezogabine on reward circuit activity and clinical symptoms in depression: Results from a randomized controlled trial","volume":"178","author":"Costi","year":"2021","journal-title":"Am. J. Psychiatry"},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.1561\/9781601985415","volume-title":"Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning","author":"Criminisi","year":"2011"},{"key":"B14","doi-asserted-by":"publisher","first-page":"7093","DOI":"10.1021\/jm501981g","article-title":"Voltage-gated sodium channels: Structure, function, pharmacology, and clinical indications","volume":"58","author":"de Lera Ruiz","year":"2015","journal-title":"J. Med. Chem."},{"key":"B15","doi-asserted-by":"publisher","first-page":"S97","DOI":"10.1097\/j.pain.0000000000000854","article-title":"Sodium channels in pain disorders: Pathophysiology and prospects for treatment","volume":"158","author":"Dib-Hajj","year":"2017","journal-title":"Pain"},{"key":"B16","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1007\/11494683_28","article-title":"Which is the best multiclass SVM method? An empirical study","volume-title":"Multiple classifier systems","author":"Duan","year":"2005"},{"key":"B17","doi-asserted-by":"publisher","first-page":"782","DOI":"10.3389\/fnins.2020.00782","article-title":"TRP channels role in pain associated with neurodegenerative diseases","volume":"14","author":"Duitama","year":"2020","journal-title":"Front. Neurosci."},{"key":"B18","doi-asserted-by":"publisher","first-page":"777607","DOI":"10.3389\/fphar.2021.777607","article-title":"Ion Channel dysfunction and neuroinflammation in migraine and depression","volume":"12","author":"Eren-Ko\u00e7ak","year":"2021","journal-title":"Front. Pharmacol."},{"key":"B19","doi-asserted-by":"publisher","first-page":"e2216482","DOI":"10.1001\/jamanetworkopen.2022.16482","article-title":"Nonopioid treatments for chronic pain\u2014integrating multimodal biopsychosocial approaches to pain management","volume":"5","author":"Eucker","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"B20","doi-asserted-by":"publisher","first-page":"e2212939","DOI":"10.1001\/jamanetworkopen.2022.12939","article-title":"Comparison of amitriptyline and us food and drug administration\u2013approved treatments for fibromyalgia: A systematic review and network meta-analysis","volume":"5","author":"Farag","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"B21","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.biopsych.2017.05.024","article-title":"Somatostatin-positive gamma-aminobutyric acid interneuron deficits in depression: Cortical microcircuit and therapeutic perspectives","volume":"82","author":"Fee","year":"2017","journal-title":"Biol. psychiatry"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e1000086","DOI":"10.1371\/journal.pgen.1000086","article-title":"Pain genes","volume":"4","author":"Foulkes","year":"2008","journal-title":"PLoS Genet."},{"key":"B23","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1002\/bies.201500143","article-title":"Extraintestinal manifestations of celiac disease: 33\u2010mer gliadin binding to glutamate receptor GRINA as a new explanation","volume":"38","author":"Garcia-Quintanilla","year":"2016","journal-title":"BioEssays"},{"key":"B24","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1111\/1756-185X.12603","article-title":"The impact of depressive and bipolar symptoms on socioeconomic status, core symptoms, function and severity of fibromyalgia","volume":"20","author":"Gota","year":"2017","journal-title":"Int. J. Rheumatic Dis."},{"key":"B25","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1038\/mp.2015.114","article-title":"Sex differences in glutamate receptor gene expression in major depression and suicide","volume":"20","author":"Gray","year":"2015","journal-title":"Mol. psychiatry"},{"key":"B26","doi-asserted-by":"publisher","first-page":"3931","DOI":"10.1523\/JNEUROSCI.4703-11.2012","article-title":"TRPM2 contributes to inflammatory and neuropathic pain through the aggravation of pronociceptive inflammatory responses in mice","volume":"32","author":"Haraguchi","year":"2012","journal-title":"J. Neurosci."},{"key":"B27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/978-981-13-1756-9_2","article-title":"TRP channels in nociception and pathological pain","volume-title":"Advances in pain research: Mechanisms and modulation of chronic pain","author":"Hung","year":"2018"},{"key":"B28","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/978-1-4757-1904-8_12","article-title":"Generalizations and adaptations of principal component analysis","volume-title":"Principal component analysis","author":"Jolliffe","year":"1986"},{"key":"B29","first-page":"89","article-title":"Genome-wide expression profiling in the peripheral blood of patients with fibromyalgia","volume":"34","author":"Jones","year":"2016","journal-title":"Clin. Exp. rheumatology"},{"key":"B30","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/35093019","article-title":"Molecular mechanisms of nociception","volume":"413","author":"Julius","year":"2001","journal-title":"Nature"},{"key":"B31","volume-title":"Principles of neural science","author":"Kandel","year":"2000"},{"key":"B32","unstructured":"Random-forest-matlab\n            KarpathyA.\n          2011"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1097\/j.pain.0000000000002510","article-title":"Potential role of blood biomarkers in patients with fibromyalgia: A systematic review with meta-analysis","volume":"163","author":"Kumbhare","year":"2022","journal-title":"Pain"},{"key":"B34","doi-asserted-by":"publisher","first-page":"116","DOI":"10.3344\/kjp.2018.31.2.116","article-title":"Prevalence of unrecognized depression in patients with chronic pain without a history of psychiatric diseases","volume":"31","author":"Lee","year":"2018","journal-title":"Korean J. Pain"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1016\/j.jad.2018.12.001","article-title":"Prevalence of depressive disorder among patients with fibromyalgia: Systematic review and meta-analysis","volume":"245","author":"L\u00f8ge-Hagen","year":"2019","journal-title":"J. Affect. Disord."},{"key":"B36","doi-asserted-by":"publisher","first-page":"S38","DOI":"10.1016\/j.jpain.2005.09.008","article-title":"Ion channel targets and treatment efficacy in neuropathic pain","volume":"7","author":"Markman","year":"2006","journal-title":"J. Pain"},{"key":"B37","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.neulet.2018.10.027","article-title":"Reduced interleukin 1A gene expression in the dorsolateral prefrontal cortex of individuals with PTSD and depression","volume":"692","author":"Morrison","year":"2019","journal-title":"Neurosci. Lett."},{"key":"B38","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s10462-017-9611-1","article-title":"Selecting training sets for support vector machines: A review","volume":"52","author":"Nalepa","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"B39","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1186\/gb-2011-12-3-218","article-title":"The transient receptor potential family of ion channels","volume":"12","author":"Nilius","year":"2011","journal-title":"Genome Biol."},{"key":"B40","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1097\/j.pain.0000000000001707","article-title":"Upregulation of cortical GABAA receptor concentration in fibromyalgia","volume":"161","author":"Pomares","year":"2020","journal-title":"Pain"},{"key":"B41","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/s11916-013-0356-5","article-title":"Worldwide epidemiology of fibromyalgia","volume":"17","author":"Queiroz","year":"2013","journal-title":"Curr. pain headache Rep."},{"key":"B42","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3389\/fpsyt.2015.00021","article-title":"Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory","volume":"6","author":"Sacchet","year":"2015","journal-title":"Front. psychiatry"},{"key":"B43","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1590\/s1807-59322011000600009","article-title":"The association of major depressive episode and personality traits in patients with fibromyalgia","volume":"66","author":"Santos","year":"2011","journal-title":"Clinics"},{"key":"B44","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1016\/j.jpain.2007.05.002","article-title":"Increased levels of neurotrophins are not specific for chronic migraine: Evidence from primary fibromyalgia syndrome","volume":"8","author":"Sarchielli","year":"2007","journal-title":"J. Pain"},{"key":"B45","doi-asserted-by":"publisher","first-page":"2758","DOI":"10.1109\/78.650102","article-title":"Comparing support vector machines with Gaussian kernels to radial basis function classifiers","volume":"45","author":"Scholkopf","year":"1997","journal-title":"Signal Process. IEEE Trans."},{"key":"B46","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","article-title":"Cytoscape: A software environment for integrated models of biomolecular interaction networks","volume":"13","author":"Shannon","year":"2003","journal-title":"Genome Res."},{"key":"B47","doi-asserted-by":"publisher","first-page":"3891","DOI":"10.3390\/ijms22083891","article-title":"Fibromyalgia: Pathogenesis, mechanisms, diagnosis and treatment options update","volume":"22","author":"Siracusa","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"B48","doi-asserted-by":"publisher","first-page":"439840","DOI":"10.1155\/2014\/439840","article-title":"Novel approach for coexpression analysis of E2F1\u20133 and MYC target genes in chronic myelogenous leukemia","volume":"2014","author":"Wang","year":"2014","journal-title":"BioMed Res. Int."},{"key":"B49","doi-asserted-by":"publisher","first-page":"610595","DOI":"10.1155\/2015\/610595","article-title":"Coexpression pattern analysis of NPM1-associated genes in chronic myelogenous leukemia","volume":"2015","author":"Wang","year":"2015","journal-title":"BioMed Res. Int."},{"key":"B50","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3389\/fgene.2016.00167","article-title":"Co-expression pattern analysis of miR-17-92 target genes in chronic myelogenous leukemia","volume":"7","author":"Wang","year":"2016","journal-title":"Front. Genet."},{"key":"B51","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1152\/physrev.00016.2015","article-title":"TRPV4: Molecular conductor of a diverse orchestra","volume":"96","author":"White","year":"2016","journal-title":"Physiol. Rev."},{"key":"B52","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1111\/j.1399-5618.2010.00848.x","article-title":"Fibromyalgia and bipolar disorder: A potential problem?","volume":"12","author":"Wilke","year":"2010","journal-title":"Bipolar Disord."},{"key":"B53","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1002\/art.1780380104","article-title":"The prevalence and characteristics of fibromyalgia in the general population","volume":"38","author":"Wolfe","year":"1995","journal-title":"Arthritis & Rheumatism Official J. Am. Coll. Rheumatology"},{"key":"B54","article-title":"Signal-to-Noise ratio (SNR)","volume-title":"Encyclopedia of radiation oncology","author":"Wong","year":"2013"},{"key":"B55","doi-asserted-by":"publisher","first-page":"1244","DOI":"10.3390\/app8081244","article-title":"Depression detection using relative EEG power induced by emotionally positive images and a conformal kernel support vector machine","volume":"8","author":"Wu","year":"2018","journal-title":"Appl. Sci."},{"key":"B56","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/diagnostics13010002","article-title":"Challenges for artificial intelligence in recognizing mental disorders","volume":"13","author":"Yan","year":"2022","journal-title":"Diagnostics"},{"key":"B57","doi-asserted-by":"publisher","first-page":"e1918377","DOI":"10.1001\/jamanetworkopen.2019.18377","article-title":"Use of machine learning for predicting escitalopram treatment outcome from electroencephalography recordings in adult patients with depression","volume":"3","author":"Zhdanov","year":"2020","journal-title":"JAMA Netw. open"},{"key":"B58","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.cca.2016.11.039","article-title":"Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine","volume":"464","author":"Zheng","year":"2017","journal-title":"Clin. Chim. acta"}],"container-title":["Frontiers in Genetics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fgene.2023.1026672\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T05:27:12Z","timestamp":1680067632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fgene.2023.1026672\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,29]]},"references-count":58,"alternative-id":["10.3389\/fgene.2023.1026672"],"URL":"https:\/\/doi.org\/10.3389\/fgene.2023.1026672","relation":{},"ISSN":["1664-8021"],"issn-type":[{"value":"1664-8021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,29]]},"article-number":"1026672"}}