Abstract
Background:
Carbohydrate sulfotransferases (CHST) were shown to be involved in carcinogenesis. The aim of the study was to assess the diagnostic value of serum CHST7 concentration in differentiation between lung cancer and non-malignant pulmonary inflammations.
Methods:
Clinical case-control study involving 125 participants was conducted: the control group containing cases of pneumonia and chronic obstructive pulmonary disease was compared to the lung cancer group composed of primary and metastatic cancers. Serum concentrations of CHST7 and routinely used markers including carcinoembryonic antigen (CEA), cytokeratin fragment 21-1 (CYFRA 21-1) and neuron-specific enolase (NSE) were determined for each participant using immunochemical methods. Statistical association, receiver operating characteristic (ROC) analysis and cross-validation were used for the evaluation of CHST7 either as a standalone biomarker or as a part of a biomarker panel.
Results:
In comparison to the control group, serum CHST7 was elevated in lung cancer (p<0.001), but no differences between the overall stages of primary cancers were detected (p=0.828). The differentiation performance in terms of ROC area under curve (AUC) was 0.848 making CHST7 superior biomarker to the NSE (p=0.031). In comparison to CEA and CYFRA 21-1, the performance differences were not detected. CHST7 was not correlated to other biomarkers, and its addition to the routine biomarker panel significantly improved the cross-validated accuracy (85.6% vs. 75.2%) and ROC AUC (p=0.004) of the differentiation using a machine learning approach.
Conclusions:
Serum CHST7 is a promising biomarker for the differentiation between lung cancer and non-malignant pulmonary inflammations.
Acknowledgments
The authors would like to thank Dario Mandić and Mirjana Fijačko, Osijek University Hospital, Croatia; Mirjana Horvat, Našice General Hospital, Croatia; and Dunja Buljubašić, Blekingesjukhuset, Karlshamn, Sweden, for assistance in sample collection and in ELISA measurements. The authors would also like to thank Larisa Miller, Sanofi Genzyme, USA, for useful comments and suggestions.
Author contributions: ŽD, SD, BD and SB conceived and designed the experiments. SD, ŽD, SM, SB and MM performed the experiments. ŽD, SD, SM, SB, LM and MS analyzed the data. ŽD, VŠ, VB and MS coordinated the research. VŠ and VB contributed reagents, materials and analysis tools. ŽD, SM, SB and SD wrote the manuscript. MM, MS, BD and LM revised the manuscript. ŽD supervised the research. All authors read and approved the final manuscript.
Research funding: This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
Employment or leadership: None declared.
Honorarium: ZD has received honorariums from Shimadzu and ChromSystems. LM has received speaker honorariums from Berlin-Chemie, Servier Pharma, Boehringer Ingelheim, Bayer Pharmacy. SD, SB, SM, MS, BD, MM, VS and VB declare receiving no relevant honorariums.
Competing interests: None declared.
References
1. Kitagawa H, Fujita M, Ito N, Sugahara K. Molecular cloning and expression of a novel chondroitin 6-O-sulfotransferase. J Biol Chem 2000;28:21075–80.10.1074/jbc.M002101200Search in Google Scholar
2. Cooney CA, Jousheghany F, Yao-Borengasser A, Phanavanh B, Gomes T, Kieber-Emmons AM, et al. Chondroitin sulfates play a major role in breast cancer metastasis: a role for CSPG4 and CHST11 gene expression in forming surface P-selectin ligands in aggressive breast cancer cells. Breast Cancer Res 2011;13:R58.10.1186/bcr2895Search in Google Scholar
3. Cattaruzza S, Schiappacassi M, Kimata K, Colombatti A, Perris R. The globular domains of PG-M/versican modulate the proliferation-apoptosis equilibrium and invasive capabilities of tumor cells. FASEB J 2004;18:779–81.10.1096/fj.03-0660fjeSearch in Google Scholar
4. Pirinen R, Leinonen T, Böhm J, Johansson R, Ropponen K, Kumpulainen E, et al. Versican in non-small cell lung cancer: relation to hyaluronan, clinicopathologic factors, and prognosis. Hum Pathol 2005;36:44–50.10.1016/j.humpath.2004.10.010Search in Google Scholar
5. Ly M, Laremore TN, Linhardt RJ. Proteoglycomics: recent progress and future challenges. OMICS 2010;14:389–99.10.1089/omi.2009.0123Search in Google Scholar
6. Stylianou M, Skandalis SS, Papadas TA, Mastronikolis NS, Theocharis DA, Papageorgakopoulou N, et al. Stage-related decorin and versican expression in human laryngeal cancer. Anticancer Res 2008;28:245–51.Search in Google Scholar
7. Suwiwat S, Ricciardelli C, Tammi R, Tammi M, Auvinen P, Kosma VM, et al. Expression of extracellular matrix components versican, chondroitin sulfate, tenascin, and hyaluronan, and their association with disease outcome in node-negative breast cancer. Clin Cancer Res 2004;10:2491–8.10.1158/1078-0432.CCR-03-0146Search in Google Scholar
8. Tammi RH, Kultti A, Kosma VM, Pirinen R, Auvinen P, Tammi MI. Hyaluronan in human tumors: pathobiological and prognostic messages from cell-associated and stromal hyaluronan. Semin Cancer Biol 2008;18:288–95.10.1016/j.semcancer.2008.03.005Search in Google Scholar
9. Wu YJ, La Pierre DP, Wu J, Yee AJ, Yang BB. The interaction of versican with its binding partners. Cell Res 2005;15:483–94.10.1038/sj.cr.7290318Search in Google Scholar
10. Ruffell B, Poon GF, Lee SS, Brown KL, Tjew SL, Cooper J, et al. Differential use of chondroitin sulfate to regulate hyaluronan binding by receptor CD44 in inflammatory and interleukin 4-activated macrophages. J Biol Chem 2011;286:19179–90.10.1074/jbc.M110.200790Search in Google Scholar
11. Pirinen R, Tammi R, Tammi M, Hirvikoski P, Parkkinen JJ, Johansson R, et al. Prognostic value of hyaluronan expression in non-small-cell lung cancer: increased stromal expression indicates unfavorable outcome in patients with adenocarcinoma. Int J Cancer 2001;95:12–7.10.1002/1097-0215(20010120)95:1<12::AID-IJC1002>3.0.CO;2-ESearch in Google Scholar
12. Vachon E, Martin R, Plumb J, Kwok V, Vandivier RW, Glogauer M, et al. CD44 is a phagocytic receptor. Blood 2006;107:4149–58.10.1182/blood-2005-09-3808Search in Google Scholar PubMed
13. Luo Z, Wu RR, Lv L, Li P, Zhang LY, Hao OL, et al. Prognostic value of CD44 expression in non-small cell lung cancer: a systematic review. Int J Clin Exp Pathol 2014;7:3632–46.Search in Google Scholar
14. Ween PM, Oehler KM, Ricciardelli C. Role of versican, hyaluronan and CD44 in ovarian cancer metastasis. Int J Mol Sci 2011;12:1009–29.10.3390/ijms12021009Search in Google Scholar PubMed PubMed Central
15. Sanchez-Palencia A, Gomez-Morales M, Gomez-Capilla JA, Pedraza V, Boyero L, Rosell R, et al. Gene expression profiling reveals novel biomarkers in non-small cell lung cancer. Int J Cancer 2011;129:355–64.10.1002/ijc.25704Search in Google Scholar PubMed
16. Hou J, Aerts J, den Hamer B, van Ijcken W, den Bakker M, Riegman P, et al. Gene expression-based classification of non-small cell lung carcinomas and survival prediction. PLoS One 2010;5:e10312.10.1371/journal.pone.0010312Search in Google Scholar PubMed PubMed Central
17. Showe MK, Vachani A, Kossenkov AV, Yousef M, Nichols C, Nikonova EV, et al. Gene expression profiles in peripheral blood mononuclear cells can distinguish patients with non-small cell lung cancer from patients with nonmalignant lung disease. Cancer Res 2009;69:9202–10.10.1158/0008-5472.CAN-09-1378Search in Google Scholar PubMed PubMed Central
18. Scott A, Salgia R. Biomarkers in lung cancer: from early detection to novel therapeutics and decision making. Biomark Med 2008;2:577–86.10.2217/17520363.2.6.577Search in Google Scholar PubMed PubMed Central
19. Qi W, Li X, Kang J. Advances in the study of serum tumor markers of lung cancer. J Canc Res Ther 2014;10:95–101.10.4103/0973-1482.145801Search in Google Scholar PubMed
20. Doseeva V, Colpitts T, Gao G, Woodcock J, Knezevic V. Performance of a multiplexed dual analyte immunoassay for the early detection of non-small cell lung cancer. J Transl Med 2015;13:55.10.1186/s12967-015-0419-ySearch in Google Scholar PubMed PubMed Central
21. Durham AL, Adcock IM. The relationship between COPD and lung cancer. Lung Cancer 2015;90:121–7.10.1016/j.lungcan.2015.08.017Search in Google Scholar PubMed PubMed Central
22. Takiguchi Y, Sekine I, Iwasawa S, Kurimoto R, Tatsumi K. Chronic obstructive pulmonary disease as a risk factor for lung cancer. World J Clin Oncol 2014;5:660–6.10.5306/wjco.v5.i4.660Search in Google Scholar PubMed PubMed Central
23. Gonzalez J, Marin M, Sanchez-Salcedo P, Zulueta JJ. Lung cancer screening in patients with chronic obstructive pulmonary disease. Ann Transl Med 2016;4:160.10.21037/atm.2016.03.57Search in Google Scholar PubMed PubMed Central
24. Punturieri A, Szabo E, Croxton TL, Shapiro SD, Dubientt SM. Lung cancer and chronic obstructive pulmonary disease: needs and opportunities for integrated research. J Natl Cancer Inst 2009;101:554–9.10.1093/jnci/djp023Search in Google Scholar PubMed PubMed Central
25. Sekine Y, Katsura H, Koh E, Hiroshima K, Fujisawa T. Early detection of COPD is important for lung cancer surveillance. Eur Respir J 2012;39:1230–40.10.1183/09031936.00126011Search in Google Scholar PubMed
26. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM, et al. Reporting recommendations for tumour MARKer prognostic studies (REMARK). Brit J Cancer 2005;9:387–91.10.1038/sj.bjc.6602678Search in Google Scholar PubMed PubMed Central
27. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Clin Chem 2015;61: 1446–52.10.1373/clinchem.2015.246280Search in Google Scholar PubMed
28. World Medical Association. Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 2013;310:2194.Search in Google Scholar
29. Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JH, Beasley MB, et al. The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol 2015;10:1243–60.10.1097/JTO.0000000000000630Search in Google Scholar PubMed
30. Chheang S, Brown K. Lung cancer staging: clinical and radiologic perspectives. Semin Intervent Radiol 2013;30:99–113.10.1055/s-0033-1342950Search in Google Scholar PubMed PubMed Central
31. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–45.10.2307/2531595Search in Google Scholar
32. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77.10.1186/1471-2105-12-77Search in Google Scholar PubMed PubMed Central
33. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics 2011;21:3940–1.10.1093/bioinformatics/bti623Search in Google Scholar PubMed
34. Breiman L. Random forests. Mach Learn 2001;45:5–32.10.1023/A:1010933404324Search in Google Scholar
35. Liaw A, Wiener M. Classification and regression by random forest. R News 2002;2:18–22.Search in Google Scholar
36. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2014.Search in Google Scholar
37. Quatromoni JG, Eruslanov E. Tumor-associated macrophages: function, phenotype and link to prognosis in human lung cancer. Am J Transl Res 2012;4:376–89.Search in Google Scholar
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