Candidate proteomic biomarkers in systemic sclerosis discovered using mass-spectrometry: an update of a systematic review (2014–2020)

1. BHATTACHARYYA S, WEI J, TOURTELLOTTE WG, HINCHCLIFF M, GOTTARDI CG, VARGA J. Fibrosis in systemic sclerosis: common and unique pathobiology. Fibrogenesis Tissue Repair. 2012; 5(Suppl 1):S18.10.1186/1755-1536-5-S1-S18336877423259815Search in Google Scholar

2. MAYEUX R. Biomarkers: potential uses and limitations. NeuroRx. 2004; 1(2):182–8.10.1602/neurorx.1.2.18253492315717018Search in Google Scholar

3. FRANTZI M, BHAT A, LATOSINSKA A. Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development. Clin Transl Med. 2014; 3(1):7.10.1186/2001-1326-3-7399424924679154Search in Google Scholar

4. BĂLĂNESCU P, LĂDARU A, BĂLĂNESCU E, BĂICUŞ C, DAN GA. Systemic sclerosis biomarkers discovered using mass-spectrometry-based proteomics: a systematic review. Biomarkers. 2014; 19(5):345–55.10.3109/1354750X.2014.92004624831309Search in Google Scholar

5. MISCHAK H, ALLMAIER G, APWEILER R, ATTWOOD T, BAUMANN M, BENIGNI A, et al. Recommendations for biomarker identification and qualification in clinical proteomics. Sci Transl Med. 2010; 2(46):46ps42.10.1126/scitranslmed.300124920739680Search in Google Scholar

6. RYU C, WALIA A, ORTIZ V, PERRY C, WOO S, REEVES BC, et al. Bioactive Plasma Mitochondrial DNA is Associated With Disease Progression in Scleroderma-Associated Interstitial Lung Disease. Arthritis Rheumatol. 2020 Jun 30. doi: 10.1002/art. 41418. Epub ahead of print.Search in Google Scholar

7. LANDI C, BARGAGLI E, CARLEO A, REFINI RM, BENNETT D, BIANCHI L, et al. Bronchoalveolar lavage proteomic analysis in pulmonary fibrosis associated with systemic sclerosis: S100A6 and 14-3-3ε as potential biomarkers. Rheumatology (Oxford). 2019; 58(1):165–178.Search in Google Scholar

8. CORALLO C, SANTUCCI A, BERNARDINI G, FIGURA N, LEONCINI R, RIOLO G, et al. Proteomic Investigation of Dermal Fibroblasts Isolated from Affected and Unaffected Skin Samples from Patients with Limited Cutaneous Systemic Sclerosis: 2 Distinct Entities? J Rheumatol. 2017; 44(1):40–48.10.3899/jrheum.16073627909082Search in Google Scholar

9. VAN BON L, COSSU M, LOOF A, GOHAR F, WITTKOWSKI H, VONK M, et al. Proteomic analysis of plasma identifies the Toll-like receptor agonists S100A8/A9 as a novel possible marker for systemic sclerosis phenotype. Ann Rheum Dis. 2014; 73(8):1585–9.10.1136/annrheumdis-2013-20501324718960Search in Google Scholar

10. CHAIRTA P, NICOLAOU P, SOKRATOUS K, GALANT C, HOUSSIAU F, OULAS A, et al. Comparative analysis of affected and unaffected areas of systemic sclerosis skin biopsies by high-throughput proteomic approaches. Arthritis Res Ther. 2020; 22(1):107.10.1186/s13075-020-02196-x720675632381114Search in Google Scholar

11. MULLENBROCK S, LIU F, SZAK S, HRONOWSKI X, GAO B, JUHASZ P, et al. Systems Analysis of Transcriptomic and Proteomic Profiles Identifies Novel Regulation of Fibrotic Programs by miRNAs in Pulmonary Fibrosis Fibroblasts. Genes (Basel). 2018; 9(12):588.10.3390/genes9120588631674330501089Search in Google Scholar

12. CHAIGNE B, CLARY G, LE GALL M, DUMOITIER N, FERNANDEZ C, LOFEK S, et al. Proteomic Analysis of Human Scleroderma Fibroblasts Response to Transforming Growth Factor-ß. Proteomics Clin Appl. 2019; 13(4):e1800069.10.1002/prca.20180006930141531Search in Google Scholar

13. DUMIT VI, KÜTTNER V, KÄPPLER J, PIERA-VELAZQUEZ S, JIMENEZ SA, BRUCKNER-TUDERMAN L, et al. Altered MCM protein levels and autophagic flux in aged and systemic sclerosis dermal fibroblasts. J Invest Dermatol. 2014; 134(9):2321–2330.10.1038/jid.2014.69412138924496236Search in Google Scholar

14. SCHILLER HB, MAYR CH, LEUSCHNER G, STRUNZ M, STAAB-WEIJNITZ C, PREISENDÖRFER S, et al. Deep Proteome Profiling Reveals Common Prevalence of MZB1-Positive Plasma B Cells in Human Lung and Skin Fibrosis. Am J Respir Crit Care Med. 2017; 196(10):1298–1310.10.1164/rccm.201611-2263OC691308628654764Search in Google Scholar

15. SCHIESS R, WOLLSCHEID B, AEBERSOLD R. Targeted proteomic strategy for clinical biomarker discovery. Mol Oncol. 2009; 3(1):33–44.10.1016/j.molonc.2008.12.001275359019383365Search in Google Scholar

16. ANDERSON NL, ANDERSON NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics. 2002; 1(11):845–67.10.1074/mcp.R200007-MCP20012488461Search in Google Scholar

17. KAYSER C, FRITZLER MJ. Autoantibodies in systemic sclerosis: unanswered questions. Front Immunol. 2015; 6:167.10.3389/fimmu.2015.00167439786225926833Search in Google Scholar

18. BROWN AJ, SEPURU KM, SAWANT KV, RAJARATHNAM K. Platelet-Derived Chemokine CXCL7 Dimer Preferentially Exists in the Glycosaminoglycan-Bound Form: Implications for Neutrophil-Platelet Crosstalk. Front Immunol. 2017; 8:1248.10.3389/fimmu.2017.01248563069529038657Search in Google Scholar

19. VAN BON L, AFFANDI AJ, BROEN J, CHRISTMANN RB, MARIJNISSEN RJ, STAWSKI L, FARINA GA, et al. Proteome-wide analysis and CXCL4 as a biomarker in systemic sclerosis. N Engl J Med. 2014 Jan 30; 370(5):433–43.10.1056/NEJMoa1114576404046624350901Search in Google Scholar

20. LUO JY, LIU X, JIANG M, ZHAO HP, ZHAO JJ. Oxidative stress markers in blood in systemic sclerosis: A meta-analysis. Mod Rheumatol. 2017; 27(2):306–314.10.1080/14397595.2016.120651027425641Search in Google Scholar

21. LEPARA Z, LEPARA O, FAJKIĆ A, REBIĆ D, ALIĆ J, SPAHOVIĆ H. Serum malondialdehyde (MDA) level as a potential biomarker of cancer progression for patients with bladder cancer. Rom J Intern Med. 2020; 58(3):146–152.10.2478/rjim-2020-000832364521Search in Google Scholar

22. KUDRYAVTSEVA AV, KRASNOV GS, DMITRIEV AA, ALEKSEEV BY, KARDYMON OL, SADRITDINOVA AF, et al. Mitochondrial dysfunction and oxidative stress in aging and cancer. Oncotarget. 2016 Jul 19; 7(29):44879–44905.10.18632/oncotarget.9821521669227270647Search in Google Scholar

23. GOLDFARB AH, GARTEN RS, WALLER J, LABBAN JD. Day to Day Variability and Reliability of Blood Oxidative Stress Markers within a Four-Week Period in Healthy Young Men. J Biomark. 2014; 2014:248313.10.1155/2014/248313443739026317028Search in Google Scholar

24. FAN X, CUI L, ZENG Y, SONG W, GAUR U, YANG M. 14-3-3 Proteins Are on the Crossroads of Cancer, Aging, and Age-Related Neurodegenerative Disease. Int J Mol Sci. 2019; 20(14):3518.10.3390/ijms20143518667893231323761Search in Google Scholar

25. YANG L, ZHANG S, WANG G. Keratin 17 in disease pathogenesis: from cancer to dermatoses. J Pathol. 2019; 247(2):158–165.Search in Google Scholar

26. BALANESCU P, LADARU A, BALANESCU E, POMPILIAN V, GOLOGANU D, CARAIOLA S, et al. Circulating Reticulocalbin 1 and Reticulocalbin 3 in Systemic Sclerosis Patients: Results of a Case Control Study. Clin Lab. 2016; 62(6):1109–16.10.7754/Clin.Lab.2015.151022Search in Google Scholar