{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T06:31:06Z","timestamp":1762065066895,"version":"build-2065373602"},"reference-count":83,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (CEEC position, 2019\u20132025 investigator)","award":["UIDB\/04462\/2020","PTDC\/BTM-TEC\/30087\/2017","PTDC\/BTM-TEC\/30088\/2017"],"award-info":[{"award-number":["UIDB\/04462\/2020","PTDC\/BTM-TEC\/30087\/2017","PTDC\/BTM-TEC\/30088\/2017"]}]},{"name":"Lisboa Portugal Regional Operational Programme (Lisboa2020)\/PORTUGAL 2020 Partnership Agreement\/European Regional Development Fund (ERDF)","award":["UIDB\/04462\/2020","PTDC\/BTM-TEC\/30087\/2017","PTDC\/BTM-TEC\/30088\/2017"],"award-info":[{"award-number":["UIDB\/04462\/2020","PTDC\/BTM-TEC\/30087\/2017","PTDC\/BTM-TEC\/30088\/2017"]}]},{"DOI":"10.13039\/501100001871","name":"FEDER funds\/COMPETE 2020 Programme and National Funds\/FCT\u2014Portuguese Foundation for Science and Technology","doi-asserted-by":"publisher","award":["UIDB\/04462\/2020","PTDC\/BTM-TEC\/30087\/2017","PTDC\/BTM-TEC\/30088\/2017"],"award-info":[{"award-number":["UIDB\/04462\/2020","PTDC\/BTM-TEC\/30087\/2017","PTDC\/BTM-TEC\/30088\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Pleural effusion (PE) is common in advanced-stage lung cancer patients and is related to poor prognosis. Identification of cancer cells is the standard method for the diagnosis of a malignant PE (MPE). However, it only has moderate sensitivity. Thus, more sensitive diagnostic tools are urgently needed. Methods: The present study aimed to discover potential protein targets to distinguish malignant pleural effusion (MPE) from other non-malignant pathologies. We have collected PE from 97 patients to explore PE proteomes by applying state-of-the-art liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers that correlate with immunohistochemistry assessment of tumor biopsy or with survival data. Functional analyses were performed to elucidate functional differences in PE proteins in malignant and benign samples. Results were integrated into a clinical risk prediction model to identify likely malignant cases. Sensitivity, specificity, and negative predictive value were calculated. Results: In total, 1689 individual proteins were identified by MS-based proteomics analysis of the 97 PE samples, of which 35 were diagnosed as malignant. A comparison between MPE and benign PE (BPE) identified 58 differential regulated proteins after correction of the p-values for multiple testing. Furthermore, functional analysis revealed an up-regulation of matrix intermediate filaments and cellular movement-related proteins. Additionally, gene ontology analysis identified the involvement of metabolic pathways such as glycolysis\/gluconeogenesis, pyruvate metabolism and cysteine and methionine metabolism. Conclusion: This study demonstrated a partial least squares regression model with an area under the curve of 98 and an accuracy of 0.92 when evaluated on the holdout test data set. Furthermore, highly significant survival markers were identified (e.g., PSME1 with a log-rank of 1.68 \u00d7 10\u22126).<\/jats:p>","DOI":"10.3390\/cancers14184366","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort"],"prefix":"10.3390","volume":"14","author":[{"given":"Sara","family":"Zahedi","sequence":"first","affiliation":[{"name":"iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ci\u00eancias M\u00e9dicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0657-1907","authenticated-orcid":false,"given":"Ana Sofia","family":"Carvalho","sequence":"additional","affiliation":[{"name":"iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ci\u00eancias M\u00e9dicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6508-8707","authenticated-orcid":false,"given":"Mostafa","family":"Ejtehadifar","sequence":"additional","affiliation":[{"name":"iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ci\u00eancias M\u00e9dicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7763-3637","authenticated-orcid":false,"given":"Hans C.","family":"Beck","sequence":"additional","affiliation":[{"name":"Department of Clinical Biochemistry, Odense University Hospital, 5000 Odense, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0131-0126","authenticated-orcid":false,"given":"N\u00e1dia","family":"Rei","sequence":"additional","affiliation":[{"name":"iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ci\u00eancias M\u00e9dicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal"}]},{"given":"Ana","family":"Luis","sequence":"additional","affiliation":[{"name":"Hospital CUF Descobertas, CUF Oncologia, 1998-018 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8556-2090","authenticated-orcid":false,"given":"Paula","family":"Borralho","sequence":"additional","affiliation":[{"name":"Hospital CUF Descobertas, CUF Oncologia, 1998-018 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3951-1134","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Bugalho","sequence":"additional","affiliation":[{"name":"iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ci\u00eancias M\u00e9dicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal"},{"name":"Hospital CUF Descobertas, CUF Oncologia, 1998-018 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6353-2616","authenticated-orcid":false,"given":"Rune","family":"Matthiesen","sequence":"additional","affiliation":[{"name":"iNOVA4Health, NOVA Medical School (NMS), Faculdade de Ci\u00eancias M\u00e9dicas (FCM), Universidade Nova de Lisboa, 1150-082 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sundaralingam, A., Bedawi, E.O., and Rahman, N.M. (2020). Diagnostics in Pleural Disease. Diagnostics, 10.","DOI":"10.3390\/diagnostics10121046"},{"key":"ref_2","first-page":"377","article-title":"Pleural Effusion in Adults\u2014Etiology, Diagnosis, and Treatment","volume":"116","author":"Jany","year":"2019","journal-title":"Dtsch. Arztebl. Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8179","DOI":"10.1007\/s11033-020-05869-7","article-title":"Liquid biopsy approaches for pleural effusion in lung cancer patients","volume":"47","author":"Baburaj","year":"2020","journal-title":"Mol. Biol. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"672747","DOI":"10.3389\/fonc.2021.672747","article-title":"Malignant Pleural Effusions\u2014A Window into Local Anti-Tumor T Cell Immunity?","volume":"11","author":"Principe","year":"2021","journal-title":"Front. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kassirian, S., Hinton, S.N., Cuninghame, S., Chaudhary, R., Iansavitchene, A., Amjadi, K., Dhaliwal, I., Zeman-Pocrnich, C., and Mitchell, M.A. (2022). Diagnostic sensitivity of pleural fluid cytology in malignant pleural effusions: Systematic review and meta-analysis. Thorax.","DOI":"10.1136\/thoraxjnl-2021-217959"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102924","DOI":"10.1016\/j.ebiom.2020.102924","article-title":"Development and validation of a novel scoring system developed from a nomogram to identify malignant pleural effusion","volume":"58","author":"Wang","year":"2020","journal-title":"eBioMedicine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"24","DOI":"10.2174\/1574887114666181204105208","article-title":"Malignant Pleural Effusion: Still a Long Way to Go","volume":"14","author":"Meriggi","year":"2019","journal-title":"Rev. Recent Clin. Trials"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.2147\/CMAR.S305223","article-title":"Predicting Survival for Patients with Malignant Pleural Effusion: Development of the CONCH Prognostic Model","volume":"2021","author":"Zhang","year":"2021","journal-title":"Cancer Manag. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13585","DOI":"10.1038\/s41598-021-93032-y","article-title":"The investigation of the volatile metabolites of lung cancer from the microenvironment of malignant pleural effusion","volume":"11","author":"Chen","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"491","DOI":"10.21037\/atm.2020.03.47","article-title":"The diagnostic yield of closed needle pleural biopsy in exudative pleural effusion: A retrospective 10-year study","volume":"8","author":"Zhang","year":"2020","journal-title":"Ann. Transl. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_12","first-page":"350","article-title":"Histologic subtype classification of non-small cell lung cancer using PET\/CT images","volume":"48","author":"Han","year":"2020","journal-title":"Eur. J. Pediatr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1038\/s41591-021-01450-2","article-title":"Toward personalized treatment approaches for non-small-cell lung cancer","volume":"27","author":"Wang","year":"2021","journal-title":"Nat. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"s41","DOI":"10.7861\/clinmedicine.18-2-s41","article-title":"Recent advances in the management of lung cancer","volume":"18","author":"Jones","year":"2018","journal-title":"Clin. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4679","DOI":"10.1038\/s41598-020-61588-w","article-title":"Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning","volume":"10","author":"Lai","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"L630","DOI":"10.1152\/ajplung.00364.2018","article-title":"Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma","volume":"316","author":"Yan","year":"2019","journal-title":"Am. J. Physiol. Cell. Mol. Physiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3596","DOI":"10.1038\/s41598-020-80735-x","article-title":"Use tumor suppressor genes as biomarkers for diagnosis of non-small cell lung cancer","volume":"11","author":"Zhang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zamay, T.N., Zamay, G.S., Kolovskaya, O.S., Zukov, R.A., Petrova, M.M., Gargaun, A., Berezovski, M.V., and Kichkailo, A.S. (2017). Current and Prospective Protein Biomarkers of Lung Cancer. Cancers, 9.","DOI":"10.3390\/cancers9110155"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1159\/000487440","article-title":"Heterogeneity in Lung Cancer","volume":"85","author":"Carvalho","year":"2018","journal-title":"Pathobiology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.cell.2020.05.043","article-title":"Integrative Proteomic Characterization of Human Lung Adenocarcinoma","volume":"182","author":"Xu","year":"2020","journal-title":"Cell"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jprot.2016.02.010","article-title":"Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data-independent acquisition and targeted data extraction","volume":"138","author":"Ortea","year":"2016","journal-title":"J. Proteom."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e1900077","DOI":"10.1002\/prca.201900077","article-title":"MS-Based Biomarker Discovery in Bronchoalveolar Lavage Fluid for Lung Cancer","volume":"14","author":"Matthiesen","year":"2019","journal-title":"Proteom. Clin. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Carvalho, A.S., Moraes, M.C.S., Na, C.H., Fierro-Monti, I., Henriques, A., Zahedi, S., Bodo, C., Tranfield, E.M., Sousa, A.L., and Farinho, A. (2020). Is the Proteome of Bronchoalveolar Lavage Extracellular Vesicles a Marker of Advanced Lung Cancer?. Cancers, 12.","DOI":"10.3390\/cancers12113450"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"srep42190","DOI":"10.1038\/srep42190","article-title":"Bronchoalveolar Lavage Proteomics in Patients with Suspected Lung Cancer","volume":"7","author":"Carvalho","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.21037\/tlcr-20-1111","article-title":"Pleural biomarkers in diagnostics of malignant pleural effusion: A narrative review","volume":"10","author":"Zhang","year":"2021","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1002\/jcp.24806","article-title":"Review: Cell Dynamics in Malignant Pleural Effusions","volume":"230","author":"Giarnieri","year":"2014","journal-title":"J. Cell. Physiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1074\/mcp.M114.045914","article-title":"In-depth Proteomic Analysis of Six Types of Exudative Pleural Effusions for Nonsmall Cell Lung Cancer Biomarker Discovery","volume":"14","author":"Liu","year":"2015","journal-title":"Mol. Cell. Proteom."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"113069","DOI":"10.1016\/j.jpba.2019.113069","article-title":"Metabolic and lipidomic characterization of malignant pleural effusion in human lung cancer","volume":"180","author":"Yang","year":"2019","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.1021\/pr5003774","article-title":"Untargeted Mass Spectrometry-Based Metabolomic Profiling of Pleural Effusions: Fatty Acids as Novel Cancer Biomarkers for Malignant Pleural Effusions","volume":"13","author":"Lam","year":"2014","journal-title":"J. Proteome Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.cca.2017.12.003","article-title":"Integrated semi-targeted metabolomics analysis reveals distinct metabolic dysregulation in pleural effusion caused by tuberculosis and malignancy","volume":"477","author":"Che","year":"2018","journal-title":"Clin. Chim. Acta"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1007\/s00432-016-2130-7","article-title":"Proteomic study of benign and malignant pleural effusion","volume":"142","author":"Li","year":"2016","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e1900001","DOI":"10.1002\/prca.201900001","article-title":"Label-Free Quantitative Proteomics Identifies Novel Biomarkers for Distinguishing Tuberculosis Pleural Effusion from Malignant Pleural Effusion","volume":"14","author":"Pan","year":"2019","journal-title":"Proteom. Clin. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"607","DOI":"10.21037\/atm.2019.09.110","article-title":"The diagnostic utility of pleural markers for tuberculosis pleural effusion","volume":"8","author":"Zhang","year":"2020","journal-title":"Ann. Transl. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.jprot.2018.09.018","article-title":"Quantitative mass spectrometry to identify protein markers for diagnosis of malignant pleural mesothelioma","volume":"192","author":"White","year":"2018","journal-title":"J. Proteom."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"225","DOI":"10.21873\/cgp.20183","article-title":"Putative Biomarkers for Malignant Pleural Mesothelioma Suggested by Proteomic Analysis of Cell Secretome","volume":"17","author":"Lacerenza","year":"2020","journal-title":"Cancer Genom. Proteom."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1158\/1055-9965.EPI-20-0543","article-title":"Verification of a Blood-Based Targeted Proteomics Signature for Malignant Pleural Mesothelioma","volume":"29","author":"Cerciello","year":"2020","journal-title":"Cancer Epidemiol. Biomark. Prev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.bbapap.2012.01.016","article-title":"Differential proteome profiling of pleural effusions from lung cancer and benign inflammatory disease patients","volume":"1824","author":"Wang","year":"2012","journal-title":"Biochim. Biophys. Acta BBA Proteins Proteom."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Choi, H., Ko, Y., and Lee, C.Y. (2020). Pro-cathepsin D as a diagnostic marker in differentiating malignant from benign pleural effusion: A retrospective cohort study. BMC Cancer, 20.","DOI":"10.1186\/s12885-020-07327-w"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Carvalho, A.S., Baeta, H., Henriques, A.F.A., Ejtehadifar, M., Tranfield, E.M., Sousa, A.L., Farinho, A., Silva, B.C., Cabe\u00e7adas, J., and Gameiro, P. (2021). Proteomic Landscape of Extracellular Vesicles for Diffuse Large B-Cell Lymphoma Subtyping. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms222011004"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3294","DOI":"10.1074\/mcp.M113.034363","article-title":"Global Mass Spectrometry and Transcriptomics Array Based Drug Profiling Provides Novel Insight into Glucosamine Induced Endoplasmic Reticulum Stress","volume":"13","author":"Carvalho","year":"2014","journal-title":"Mol. Cell. Proteom."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1038\/nbt.1511","article-title":"MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification","volume":"26","author":"Cox","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4176","DOI":"10.1016\/j.jprot.2012.05.010","article-title":"SIR: Deterministic protein inference from peptides assigned to MS data","volume":"75","author":"Matthiesen","year":"2012","journal-title":"J. Proteom."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2202\/1544-6115.1027","article-title":"Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments","volume":"3","author":"Smyth","year":"2004","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e10427","DOI":"10.15252\/emmm.201910427","article-title":"Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies","volume":"11","author":"Geyer","year":"2019","journal-title":"EMBO Mol. Med."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Therneau, T.M., and Grambsch, P.M. (2000). Modeling Survival Data: Extending the Cox Model, Springer.","DOI":"10.1007\/978-1-4757-3294-8"},{"key":"ref_47","unstructured":"Kosinski, M., and Biecek, P. (2022, July 15). RTCGA: The Cancer Genome Atlas Data Integration. R Package Version 1.26.0. Available online: https:\/\/www.bioconductor.org\/packages\/devel\/bioc\/manuals\/RTCGA\/man\/RTCGA.pdf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., and M\u00fcller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-77"},{"key":"ref_49","unstructured":"Kuhn, M. (2022, July 15). caret: Classification and Regression Training. R Package Version 6.0-88. Available online: https:\/\/cran.r-project.org\/web\/packages\/caret\/caret.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Klein, J.P., Van Houwelingen, H.C., Ibrahim, J.G., and Scheike, T.H. (2016). Handbook of Survival Analysis, Chapman and Hall.","DOI":"10.1201\/b16248"},{"key":"ref_51","first-page":"2657","article-title":"Prognostic value of the mRNA expression of members of the HSP90 family in non-small cell lung cancer","volume":"17","author":"Liu","year":"2019","journal-title":"Exp. Ther. Med."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s13569-016-0057-z","article-title":"High nuclear expression of proteasome activator complex subunit 1 predicts poor survival in soft tissue leiomyosarcomas","volume":"6","author":"Lou","year":"2016","journal-title":"Clin. Sarcoma Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/s40484-016-0081-2","article-title":"Performance measures in evaluating machine learning based bioinformatics predictors for classifications","volume":"4","author":"Jiao","year":"2016","journal-title":"Quant. Biol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Xu, B., Wang, Y., Wang, Z., Zhou, J., Zhou, S., and Guan, J. (2017). An effective approach to detecting both small and large complexes from protein-protein interaction networks. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1820-8"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lim, Y., Yu, I.-J., Seo, D., Kang, U., and Sael, L. (2019). PS-MCL: Parallel shotgun coarsened Markov clustering of protein interaction networks. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-2856-8"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Broh\u00e9e, S., and Van Helden, J. (2006). Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-488"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.cels.2016.02.015","article-title":"Plasma Proteome Profiling to Assess Human Health and Disease","volume":"2","author":"Geyer","year":"2016","journal-title":"Cell Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.1038\/s41598-022-06924-y","article-title":"Diagnosing pleural effusions using mass spectrometry-based multiplexed targeted proteomics quantitating mid- to high-abundance markers of cancer, infection\/inflammation and tuberculosis","volume":"12","author":"Robak","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1021\/pr4012377","article-title":"Targeted Proteomics Pipeline Reveals Potential Biomarkers for the Diagnosis of Metastatic Lung Cancer in Pleural Effusion","volume":"13","author":"Chen","year":"2014","journal-title":"J. Proteome Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Cappellesso, R., Millioni, R., Arrigoni, G., Simonato, F., Caroccia, B., Iori, E., Guzzardo, V., Ventura, L., Tessari, P., and Fassina, A. (2015). Lumican Is Overexpressed in Lung Adenocarcinoma Pleural Effusions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0126458"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"123","DOI":"10.2217\/bmm-2018-0200","article-title":"Potential biomarkers for antidiastole of tuberculous and malignant pleural effusion by proteome analysis","volume":"13","author":"Shi","year":"2019","journal-title":"Biomark. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.bbrc.2014.12.083","article-title":"Proteome screening of pleural effusions identifies IL1A as a diagnostic biomarker for non-small cell lung cancer","volume":"457","author":"Li","year":"2015","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s12094-013-1054-9","article-title":"Proteomic analysis of pleural effusion from lung adenocarcinoma patients by shotgun strategy","volume":"16","author":"Sheng","year":"2014","journal-title":"Clin. Transl. Oncol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4671","DOI":"10.1021\/pr2004743","article-title":"Comprehensive Proteome Analysis of Malignant Pleural Effusion for Lung Cancer Biomarker Discovery by Using Multidimensional Protein Identification Technology","volume":"10","author":"Yu","year":"2011","journal-title":"J. Proteome Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"S152","DOI":"10.1097\/JTO.0b013e318174ea3a","article-title":"Heat Shock Protein 90 Inhibition in Lung Cancer","volume":"3","author":"Shimamura","year":"2008","journal-title":"J. Thorac. Oncol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s13058-021-01423-w","article-title":"Inhibition of stromal biglycan promotes normalization of the tumor microenvironment and enhances chemotherapeutic efficacy","volume":"23","author":"Cong","year":"2021","journal-title":"Breast Cancer Res. BCR"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hassan, M.K., Kumar, D., Naik, M., and Dixit, M. (2018). The expression profile and prognostic significance of eukaryotic translation elongation factors in different cancers. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0191377"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"112991","DOI":"10.1016\/j.jim.2021.112991","article-title":"Identification of tumor-associated antigens of lung cancer: SEREX combined with bioinformatics analysis","volume":"492","author":"Wang","year":"2021","journal-title":"J. Immunol. Methods"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Guinde, J., Frankel, D., Perrin, S., Delecourt, V., L\u00e9vy, N., Barlesi, F., Astoul, P., Roll, P., and Kaspi, E. (2018). Lamins in Lung Cancer: Biomarkers and Key Factors for Disease Progression through miR-9 Regulation?. Cells, 7.","DOI":"10.3390\/cells7070078"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"7370","DOI":"10.1158\/1078-0432.CCR-07-0747","article-title":"Proteomic Profiling Identifies Afamin as a Potential Biomarker for Ovarian Cancer","volume":"13","author":"Jackson","year":"2007","journal-title":"Clin. Cancer Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e11621","DOI":"10.7554\/eLife.11621","article-title":"Active and water-soluble form of lipidated Wnt protein is maintained by a serum glycoprotein afamin\/\u03b1-albumin","volume":"5","author":"Mihara","year":"2016","journal-title":"eLife"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.cca.2015.04.010","article-title":"Afamin\u2014A pleiotropic glycoprotein involved in various disease states","volume":"446","author":"Dieplinger","year":"2015","journal-title":"Clin. Chim. Acta"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"20605","DOI":"10.1038\/srep20605","article-title":"Breast Cancer MDA-MB-231 Cells Use Secreted Heat Shock Protein-90alpha (Hsp90\u03b1) to Survive a Hostile Hypoxic Environment","volume":"6","author":"Dong","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1379\/CSC-99r.1","article-title":"Heat shock proteins in cancer: Diagnostic, prognostic, predictive, and treatment implications","volume":"10","author":"Ciocca","year":"2005","journal-title":"Cell Stress Chaperon"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"663001","DOI":"10.3389\/fcell.2021.663001","article-title":"The Expression Patterns and Prognostic Value of the Proteasome Activator Subunit Gene Family in Gastric Cancer Based on Integrated Analysis","volume":"9","author":"Guo","year":"2021","journal-title":"Front. Cell Dev. Biol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.cell.2018.02.052","article-title":"An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics","volume":"173","author":"Liu","year":"2018","journal-title":"Cell"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1158\/2159-8290.CD-12-0095","article-title":"The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data","volume":"2","author":"Cerami","year":"2012","journal-title":"Cancer Discov."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1038\/s41416-018-0038-5","article-title":"The multifunctional solute carrier 3A2 (SLC3A2) confers a poor prognosis in the highly proliferative breast cancer subtypes","volume":"118","author":"Craze","year":"2018","journal-title":"Br. J. Cancer"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhou, X., Curbo, S., Li, F., Krishnan, S., and Karlsson, A. (2018). Inhibition of glutamate oxaloacetate transaminase 1 in cancer cell lines results in altered metabolism with increased dependency of glucose. BMC Cancer, 18.","DOI":"10.1186\/s12885-018-4443-1"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Whitney, D.H., Elashoff, M.R., Porta-Smith, K., Gower, A.C., Vachani, A., Ferguson, J.S., Silvestri, G.A., Brody, J.S., Lenburg, M.E., and Spira, A. (2015). Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC Med. Genom., 8.","DOI":"10.1186\/s12920-015-0091-3"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"5469","DOI":"10.1038\/ncomms6469","article-title":"Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact","volume":"5","author":"Li","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.chest.2018.02.012","article-title":"Assessment of Plasma Proteomics Biomarker\u2019s Ability to Distinguish Benign from Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial","volume":"154","author":"Silvestri","year":"2018","journal-title":"Chest"},{"key":"ref_83","first-page":"D543","article-title":"The PRIDE database resources in 2022: A hub for mass spectrometry-based proteomics evidences","volume":"50","author":"Bai","year":"2021","journal-title":"Nucleic Acids Res."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/18\/4366\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:25:29Z","timestamp":1760142329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/18\/4366"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,8]]},"references-count":83,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["cancers14184366"],"URL":"https:\/\/doi.org\/10.3390\/cancers14184366","relation":{},"ISSN":["2072-6694"],"issn-type":[{"type":"electronic","value":"2072-6694"}],"subject":[],"published":{"date-parts":[[2022,9,8]]}}}