A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer

Abstract

Background: Gastric cancer (GC) is the third leading cause of cancer death. Early detection is a key factor to reduce its mortality.

Methods: We retrospectively collected pre- and postoperative serum samples as well as tumour tissues and adjacent normal tissues from 100 GC patients. Serum samples from non-cancerous patients were served as controls (n = 50). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure levels of over 300 proteins. Alteration of each protein was analysed by univariate analysis. Elastic-net logistic regression was performed to select serum proteins into the diagnostic model.

Findings: We identified 19 serum proteins (CEACAM5, CA9, MSLN, CCL20, SCF, TGF-alpha, MMP-1, MMP-10, IGF-1, CDCP1, PPIA, DDAH-1, HMOX-1, FLI1, IL-7, ZBTB-17, APBB1IP, KAZALD-1, and ADAMTS-15) that together distinguish GC cases from controls with a diagnostic sensitivity of 93%, specificity of 100%, and area under receiver operating characteristic curve (AUC) of 0·99 (95% CI: 0·98-1). Moreover, the 19-serum protein signature provided an increased diagnostic capacity in patients at TNM I-II stage (sensitivity 89%, specificity 100%, AUC 0·99) and in patients with high microsatellite instability (MSI) (91%, 98%, and 0·99) compared to individual proteins. These promising results will inspire a large-scale independent cohort study to be pursued for validating the proposed protein signature.

Interpretation: Based on targeted proteomics and elastic-net logistic regression, we identified a 19-serum protein signature which could contribute to clinical GC diagnosis, especially for patients at early stage and those with high MSI. FUND: This study was supported by a European H2020-Marie Skłodowska-Curie Innovative Training Networks grant (316,929, GastricGlycoExplorer). Funder had no influence on trial design, data evaluation, and interpretation.

Keywords: Biomarker; Diagnosis; Gastric cancer; PEA; Proteomics.

Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1

A schematic diagram overview of the study. GC, gastric cancer. PEA, proximity extension assay. LOD, limit of detection.

Fig. 2

Multiplex PEA results of proteins measured in GC tissues and serum specimen. (A) Venn diagram showing the number of proteins detectable or undetectable in serum and in tissue specimen. (B) Comparison of subcellular location between proteins detectable in serum but not in tissue and proteins detectable in tissue but not in serum by the FunRich software. The 13 proteins detectable in GC tissue but not in serum are AGR3, ARTN, CAMKK1, IL1A, IL20RA, IL22RA1, IL24, IL33, JUN, LIF, NCLN, NRTN, PAK4. The 14 proteins detectable in GC serum but not in tissue are CRX, DKKL1, FAM19A5, FCRLB, FGF23, IL10, IL10RA, IL2, LYPD1, OPTC, SEZ6L, SLITRK2, TCL1B, WNT9A. (C) Principal component analysis (PCA) plot illustrating the sample distribution of 100 gastric tumour tissues (T, blue) and matched adjacent noncancerous tissues (N, red), based on 245 proteins levels. Each dot represents an individual sample. (D) Volcano plot showing the 245 proteins levels in GC tissues compared to matched non-tumour tissues. The dashed line represents the cutoff line with indicated significance criteria. Points having absolute log fold-change ≥2 and FDR adjusted p-value p-value ≥0·05 are in gray, and the rest are in black. (E) PCA plot illustrating the distribution of 50 serum samples from controls (Ctrl, red), 100 GC preoperative serum samples (Pre, green) and matched 100 postoperative samples (Post, blue), based on 316 proteins levels. (F—H) Volcano plots showing the 316 protein levels in preoperative GC serum samples versus controls (F), between preoperative and postoperative ones (G), and between postoperative samples and controls (H). Points having absolute log fold-change ≥0·5 and FDR adjusted p-value <0·05 are shown in red, with absolute log fold-change <0·5 and p-value <0·05 are in black, with absolute log fold-change <0·5 and p-value ≥0·05 are in gray, and the rest are in orange. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Diagnostic capacity for gastric cancer of the identified 19 serum protein signature by elastic-net logistic regression. (A) Diagnostic performances of different protein combinations. Proteins are sorted according to the absolute coefficient from the largest to the smallest. “ROC test P" is the p-value of the comparison of ROC curves generated from successive protein combinations with one more protein added at a time. Coef., coefficient. ROC, receiver operator characteristics. *, p < 0·05. (B) Overlaid ROC curves of each of the combinations from two to 19 serum proteins. Comb, combination. (C) Protein-protein interactions among the 19 proteins assessed with the STRING database.

Fig. 4

Protein levels in serum samples from GC patients at TNM I-II stage or with high microsatellite instability (MSI) status. (A) Volcano plot showing the comparison of protein levels between patients at TNM I-II stage and controls. (B) ROC curves for the 19-serum protein signature overlaid with each individual protein showing the diagnostic capacity of GC at TNM I-II stage versus controls. (C) Volcano plot showing the comparison of protein levels between patients with MSI-H and controls. (D) ROC curves for the 19-serum protein signature overlaid with each individual protein showing the diagnostic capacity of GC with MSI-H status versus controls. Points in plots A and C having absolute log fold-change ≥0·5 and false discovery rate adjusted p-value

References

1. 1. Bray F., Ferlay J., Soerjomataram I., Siegel R., Torre L., Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A J Clin. 2018;00:1–31.
   2. Bray F, Ferlay J, Soerjomataram I, Siegel R, Torre L, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A J Clin 2018;00:1–31. doi:10.3322/caac.21492.
2. 1. Pasechnikov V., Chukov S., Fedorov E., Kikuste I., Leja M. Gastric cancer: prevention, screening and early diagnosis. World J Gastroenterol. 2014;20:13842–13862.
   2. Pasechnikov V, Chukov S, Fedorov E, Kikuste I, Leja M. Gastric cancer: Prevention, screening and early diagnosis. World J Gastroenterol 2014;20:13842–62. doi:10.3748/wjg.v20.i38.13842.
3. 1. Marrelli D., Polom K., De Manzoni G., Morgagni P., Baiocchi G.L., Roviello F. Multimodal treatment of gastric cancer in the west: where are we going? World J Gastroenterol. 2015;21:7954–7969.
   2. Marrelli D, Polom K, De Manzoni G, Morgagni P, Baiocchi GL, Roviello F. Multimodal treatment of gastric cancer in the west: Where are we going? World J Gastroenterol 2015;21:7954–69. doi:10.3748/wjg.v21.i26.7954.
4. 1. Liu W., Yang Q., Liu B., Zhu Z. Serum proteomics for gastric cancer. Clin Chim Acta. 2014;431:179–184.
   2. Liu W, Yang Q, Liu B, Zhu Z. Serum proteomics for gastric cancer. Clin Chim Acta 2014;431:179–84. doi:10.1016/j.cca.2014.02.001.
5. 1. Ucar E., Semerci E., Ustun H., Yetim T., Huzmeli C., Gullu M. Prognostic value of preoperative CEA, CA 19-9, CA 72-4, and AFP levels in gastric cancer. Adv Ther. 2008;25:1075–1084.
   2. Ucar E, Semerci E, Ustun H, Yetim T, Huzmeli C, Gullu M. Prognostic value of preoperative CEA, CA 19-9, CA 72-4, and AFP levels in gastric cancer. Adv Ther 2008;25:1075–84. doi:10.1007/s12325-008-0100-4.
6. 1. Leung W.K., Wu M.S., Kakugawa Y., Kim J.J., Yeoh K.G., Goh K.L. Screening for gastric cancer in Asia: current evidence and practice. Lancet Oncol. 2008;9:279–287.
   2. Leung WK, Wu MS, Kakugawa Y, Kim JJ, Yeoh KG, Goh KL, et al. Screening for gastric cancer in Asia: current evidence and practice. Lancet Oncol 2008;9:279–87. doi:10.1016/S1470-2045(08)70072-X.
7. 1. Huang Y.K., Yu J.C., Kang W.M., Ma Z.Q., Ye X., Tian S.B. Significance of serum pepsinogens as a biomarker for gastric cancer and atrophic gastritis screening: A systematic review and meta-analysis. PLoS One. 2015;10
   2. Huang YK, Yu JC, Kang WM, Ma ZQ, Ye X, Tian SB, et al. Significance of serum pepsinogens as a biomarker for gastric cancer and atrophic gastritis screening: A systematic review and meta-analysis. PLoS One 2015;10. doi:10.1371/journal.pone.0142080.
8. 1. Kang C., Lee Y., Lee J.E. Recent advances in mass spectrometry-based proteomics of gastric cancer. World J Gastroenterol. 2016;22:8283–8293.
   2. Kang C, Lee Y, Lee JE. Recent advances in mass spectrometry-based proteomics of gastric cancer. World J Gastroenterol 2016;22:8283–93. doi:10.3748/wjg.v22.i37.8283.
9. 1. Assarsson E., Lundberg M., Holmquist G., Björkesten J., Thorsen S.B., Ekman D. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One. 2014;9
   2. Assarsson E, Lundberg M, Holmquist G, Björkesten J, Thorsen SB, Ekman D, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One 2014;9:e95192. doi:10.1371/journal.pone.0095192.
10. 1. Fredriksson S., Gullberg M., Jarvius J., Olsson C., Pietras K., Gústafsdóttir S.M. Protein detection using proximity-dependent DNA ligation assays. Nat Biotechnol. 2002;20:473–477.
    2. Fredriksson S, Gullberg M, Jarvius J, Olsson C, Pietras K, Gústafsdóttir SM, et al. Protein detection using proximity-dependent DNA ligation assays. Nat Biotechnol 2002;20:473–7. doi:10.1038/nbt0502-473.
11. 1. Lundberg M., Eriksson A., Tran B., Assarsson E., Fredriksson S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 2011;39:e102.
    2. Lundberg M, Eriksson A, Tran B, Assarsson E, Fredriksson S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res 2011;39:e102. doi:10.1093/nar/gkr424.
12. 1. Conze T., Carvalho A.S., Landegren U., Almeida R., Reis C.A., David L. MUC2 mucin is a major carrier of the cancer-associated sialyl-Tn antigen in intestinal metaplasia and gastric carcinomas. Glycobiology. 2009;20:199–206.
    2. Conze T, Carvalho AS, Landegren U, Almeida R, Reis CA, David L, et al. MUC2 mucin is a major carrier of the cancer-associated sialyl-Tn antigen in intestinal metaplasia and gastric carcinomas. Glycobiology 2009;20:199–206. doi:10.1093/glycob/cwp161.
13. 1. de Oliveira F.M.S., Mereiter S., Lönn P., Siart B., Shen Q., Heldin J. Detection of post-translational modifications using solid-phase proximity ligation assay. N Biotechnol. 2018:51–59.
    2. Oliveira FMS de, Mereiter S, Lönn P, Siart B, Shen Q, Heldin J, et al. Detection of post-translational modifications using solid-phase proximity ligation assay. N Biotechnol 2018:51–9. doi:10.1016/j.nbt.2017.10.005.
14. 1. Bossuyt P.M., Reitsma J.B., Bruns D.E., Gatsonis C.A., Glasziou P.P., Irwig L. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Clin Chem. 2015;61:1446–1452.
    2. 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. doi:10.1373/clinchem.2015.246280.
15. 1. Lauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. Acta Pathol Microbiol Scand. 1965;64:31–49.
    2. Lauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. Acta Pathol Microbiol Scand 1965;64:31–49. doi:10.1002/1097-0142(197706)39:6<2475::AID-CNCR2820390626>;2-L.
16. 1. Watanabe H., Jass J.R., Sobin L.H. Histol. Typing oesophageal gastric tumours. Springer Berlin Heidelberg; Berlin, Heidelberg: 1990. Histological classification of oesophageal tumours; pp. 5–6.
    2. Watanabe H, Jass JR, Sobin LH. Histological Classification of Oesophageal Tumours. Histol. Typing Oesophageal Gastric Tumours, Berlin, Heidelberg: Springer Berlin Heidelberg; 1990, p. 5–6. doi:10.1007/978-3-642-83920-7_2.
17. 1. Edge S.B. Springer; 2010. American joint committee on Cancer. AJCC cancer staging manual.
    2. Edge SB, American Joint Committee on Cancer. AJCC cancer staging manual. Springer; 2010.
18. 1. Corso G., Velho S., Paredes J., Pedrazzani C., Martins D., Milanezi F. Oncogenic mutations in gastric cancer with microsatellite instability. Eur J Cancer. 2011;47:443–451.
    2. Corso G, Velho S, Paredes J, Pedrazzani C, Martins D, Milanezi F, et al. Oncogenic mutations in gastric cancer with microsatellite instability. Eur J Cancer 2011;47:443–51. doi:10.1016/j.ejca.2010.09.008.
19. 1. R core team R: A language and environment for statistical computing. R found stat Comput Vienna, Austria. 2017.
    2. R core team. R: A language and environment for statistical computing. R Found Stat Comput Vienna, Austria 2017. doi:.
20. 1. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289–300.
    2. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995;57:289–300. doi:10.2307/2346101.
21. 1. Robin X., Turck N., Hainard A., Tiberti N., Lisacek F., Sanchez J.C. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinforma. 2011;12
    2. 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. doi:10.1186/1471-2105-12-77.
22. 1. Sing T., Sander O., Beerenwinkel N., Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21:3940–3941.
    2. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: Visualizing classifier performance in R. Bioinformatics 2005;21:3940–1. doi:10.1093/bioinformatics/bti623.
23. 1. Friedman J., Hastie T., Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33
    2. Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 2010;33. doi:10.18637/jss.v033.i01.
24. 1. Szklarczyk D., Franceschini A., Wyder S., Forslund K., Heller D., Huerta-Cepas J. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–D452.
    2. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015;43:D447–52. doi:10.1093/nar/gku1003.
25. 1. Pathan M., Keerthikumar S., Ang C.S., Gangoda L., Quek C.Y.J., Williamson N.A. FunRich: an open access standalone functional enrichment and interaction network analysis tool. Proteomics. 2015;15:2597–2601.
    2. Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CYJ, Williamson NA, et al. FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics 2015;15:2597–601. doi:10.1002/pmic.201400515.
26. 1. Cristescu R., Lee J., Nebozhyn M., Kim K.-M., Ting J.C., Wong S.S. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med. 2015;21:449–456.
    2. Cristescu R, Lee J, Nebozhyn M, Kim K-M, Ting JC, Wong SS, et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med 2015;21:449–56. doi:10.1038/nm.3850.
27. 1. Cancer T., Atlas G., Bass A.J., Thorsson V.V., Shmulevich I., Reynolds S.M. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513:202–209.
    2. Cancer T, Atlas G, Bass AJ, Thorsson VV, Shmulevich I, Reynolds SM, et al. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 2014;513:202–9. doi:10.1038/nature13480.
28. 1. Mereiter S., Polom K., Williams C., Polonia A., Guergova-Kuras M., Karlsson N. The Thomsen-Friedenreich antigen: A highly sensitive and specific predictor of microsatellite instability in gastric Cancer. J Clin Med. 2018
    2. Mereiter S, Polom K, Williams C, Polonia A, Guergova-Kuras M, Karlsson N, et al. The Thomsen-Friedenreich Antigen: A Highly Sensitive and Specific Predictor of Microsatellite Instability in Gastric Cancer. J Clin Med 2018. doi:10.3390/jcm7090256.
29. 1. Kim M., Kim H.J., Choi B.Y., Kim J.-H., Song K.-S., Noh S.-M. Identification of potential serum biomarkers for gastric cancer by a novel computational method, multiple normal tissues corrected differential analysis. Clin Chim Acta. 2012;413:428–433.
    2. Kim M, Kim HJ, Choi BY, Kim J-H, Song K-S, Noh S-M, et al. Identification of potential serum biomarkers for gastric cancer by a novel computational method, multiple normal tissues corrected differential analysis. Clin Chim Acta 2012;413:428–33. doi:10.1016/j.cca.2011.10.026.
30. 1. Murray G.I., Duncan M.E., Arbuckle E., Melvin W.T., Fothergill J.E. Matrix metalloproteinases and their inhibitors in gastric cancer. Gut. 1998;43:791–797.
    2. Murray GI, Duncan ME, Arbuckle E, Melvin WT, Fothergill JE. Matrix metalloproteinases and their inhibitors in gastric cancer. Gut 1998;43:791–7. doi:10.1136/gut.43.6.791.
31. 1. Xu J., E C., Yao Y., Ren S., Wang G., Jin H. Matrix metalloproteinase expression and molecular interaction network analysis in gastric cancer. Oncol Lett. 2016;12:2403–2408.
    2. Xu J, E C, Yao Y, Ren S, Wang G, Jin H. Matrix metalloproteinase expression and molecular interaction network analysis in gastric cancer. Oncol Lett 2016;12:2403–8. doi:10.3892/ol.2016.5013.
32. 1. Han S.H., Joo M., Kim H., Chang S. Mesothelin expression in gastric adenocarcinoma and its relation to clinical outcomes. J Pathol Transl Med. 2017;51:122–128.
    2. Han SH, Joo M, Kim H, Chang S. Mesothelin expression in gastric adenocarcinoma and its relation to clinical outcomes. J Pathol Transl Med 2017;51:122–8. doi:10.4132/jptm.2016.11.18.
33. 1. B K., I S., A T., U Y., O H., M M. Mesothelin expression correlates with prolonged patient survival in gastric cancer. J Surg Oncol. 2012;105:195–199.
    2. K. B, S. I, T. A, Y. U, H. O, M. M, et al. Mesothelin expression correlates with prolonged patient survival in gastric cancer. J Surg Oncol 2012;105:195–9. doi:10.1002/jso.22024.
34. 1. Ito T., Kajino K., Abe M., Sato K., Maekawa H., Sakurada M. ERC/mesothelin is expressed in human gastric cancer tissues and cell lines. Oncol Rep. 2014;31:27–33.
    2. Ito T, Kajino K, Abe M, Sato K, Maekawa H, Sakurada M, et al. ERC/mesothelin is expressed in human gastric cancer tissues and cell lines. Oncol Rep 2014;31:27–33. doi:10.3892/or.2013.2803.
35. 1. Fidan E., Mentese A., Ozdemir F., Deger O., Kavgaci H., Caner Karahan S. Diagnostic and prognostic significance of CA IX and suPAR in gastric cancer. Med Oncol. 2013;30
    2. Fidan E, Mentese A, Ozdemir F, Deger O, Kavgaci H, Caner Karahan S, et al. Diagnostic and prognostic significance of CA IX and suPAR in gastric cancer. Med Oncol 2013;30. doi:10.1007/s12032-013-0540-9.
36. 1. Chen J., Röcken C., Hoffmann J., Krüger S., Lendeckel U., Rocco A. Expression of carbonic anhydrase 9 at the invasion front of gastric cancers. Gut. 2005;54:920–927.
    2. Chen J, Röcken C, Hoffmann J, Krüger S, Lendeckel U, Rocco A, et al. Expression of carbonic anhydrase 9 at the invasion front of gastric cancers. Gut 2005;54:920–7. doi:10.1136/gut.2004.047340.
37. 1. Leppilampi M., Saarnio J., Karttunen T.J., Kivelä J., Pastoreková S., Pastorek J. Carbonic anhydrase isozymes IX and XII in gastric tumors. World J Gastroenterol. 2003;9:1398–1403.
    2. Leppilampi M, Saarnio J, Karttunen TJ, Kivelä J, Pastoreková S, Pastorek J, et al. Carbonic anhydrase isozymes IX and XII in gastric tumors. World J Gastroenterol 2003;9:1398–403. doi:10.3748/wjg.v9.i7.1398.
38. 1. Rajkumar T., Vijayalakshmi N., Gopal G., Sabitha K., Shirley S., Raja U.M. Identification and validation of genes involved in gastric tumorigenesis. Cancer Cell Int. 2010;10
    2. Rajkumar T, Vijayalakshmi N, Gopal G, Sabitha K, Shirley S, Raja UM, et al. Identification and validation of genes involved in gastric tumorigenesis. Cancer Cell Int 2010;10. doi:10.1186/1475-2867-10-45.
39. 1. Yoshida A., Isomoto H., Hisatsune J., Nakayama M., Nakashima Y., Matsushima K. Enhanced expression of CCL20 in human helicobacter pylori-associated gastritis. Clin Immunol. 2009;130:290–297.
    2. Yoshida A, Isomoto H, Hisatsune J, Nakayama M, Nakashima Y, Matsushima K, et al. Enhanced expression of CCL20 in human Helicobacter pylori-associated gastritis. Clin Immunol 2009;130:290–7. doi:10.1016/j.clim.2008.09.016.
40. 1. Raja U.M., Gopal G., Shirley S., Ramakrishnan A.S., Rajkumar T. Immunohistochemical expression and localization of cytokines/chemokines/growth factors in gastric cancer. Cytokine. 2017;89:82–90.
    2. Raja UM, Gopal G, Shirley S, Ramakrishnan AS, Rajkumar T. Immunohistochemical expression and localization of cytokines/chemokines/growth factors in gastric cancer. Cytokine 2017;89:82–90. doi:10.1016/j.cyto.2016.08.032.
41. 1. Lim J.B., Kim D.K., Chung H.W. Clinical significance of serum thymus and activation-regulated chemokine in gastric cancer: potential as a serum biomarker. Cancer Sci. 2014;105:1327–1333.
    2. Lim JB, Kim DK, Chung HW. Clinical significance of serum thymus and activation-regulated chemokine in gastric cancer: Potential as a serum biomarker. Cancer Sci 2014;105:1327–33. doi:10.1111/cas.12505.
42. 1. Zhong B., Li Y., Liu X., Wang D. Association of mast cell infiltration with gastric cancer progression. Oncol Lett. 2017;15:755–764.
    2. Zhong B, Li Y, Liu X, Wang D. Association of mast cell infiltration with gastric cancer progression. Oncol Lett 2017;15:755–64. doi:10.3892/ol.2017.7380.
43. 1. Moskal T., Huang S., Ellis L.M., Fritsche A., Chakrabarty S. Serum levels of transforming growth factor Cancer in gastrointestinal. Cancer Epidemiol Biomarkers Prev. 1995;4:127–131.
    2. Moskal T, Huang S, Ellis LM, Fritsche A, Chakrabarty S. Serum Levels of Transforming Growth Factor Cancer in Gastrointestinal. Cancer Epidemiol Biomarkers Prev 1995;4:127–31.
44. 1. Choi J., Chul H., Lim H., Ki D. vol. 749. 1999. Detection of transforming growth factor- · in the serum of gastric carcinoma patients; pp. 236–241.
    2. Choi J, Chul H, Lim H, Ki D. Detection of Transforming Growth Factor- · in the Serum of Gastric Carcinoma Patients 1999;749:236–41.
45. 1. Konturek P.C., Konturek S.J., Sulekova Z., Meixner H., Karczewska E., Hahn E.G. Expression of hepatocyte growth factor, transforming growth factor alpha, apoptosis related proteins Bax and Bcl-2, and gastrin in human gastric cancer. Aliment Pharmacol Ther. 2001;15:989–999.
    2. Konturek PC, Konturek SJ, Sulekova Z, Meixner H, Karczewska E, Hahn EG, et al. Expression of hepatocyte growth factor, transforming growth factor alpha, apoptosis related proteins Bax and Bcl-2, and gastrin in human gastric cancer Aliment Pharmacol Ther. 2001;15:989–99.
46. 1. Pham T.M., Fujino Y., Kikuchi S., Tamakoshi A., Yatsuya H., Matsuda S. A nested case-control study of stomach cancer and serum insulin-like growth factor (IGF)-1, IGF-2 and IGF-binding protein (IGFBP)-3. Eur J Cancer. 2007;43:1611–1616.
    2. Pham TM, Fujino Y, Kikuchi S, Tamakoshi A, Yatsuya H, Matsuda S, et al. A nested case-control study of stomach cancer and serum insulin-like growth factor (IGF)-1, IGF-2 and IGF-binding protein (IGFBP)-3. Eur J Cancer 2007;43:1611–6. doi:10.1016/j.ejca.2007.04.014.
47. 1. Gu M.-J., Bae Y.-K., Choi J.H. Clinical significance of insulin-growth factor 1 and insulin-growth factor 1 receptor expression in gastrointestinal stromal tumors. Hepatogastroenterology. 2013;60:1383–1386.
    2. Gu M-J, Bae Y-K, Choi JH. Clinical significance of insulin-growth factor 1 and insulin-growth factor 1 receptor expression in gastrointestinal stromal tumors. Hepatogastroenterology 2013;60:1383–6.
48. 1. Aung P., Oue N., Mitani Y., Nakayama H., Yoshida K., Noguchi T. Systematic search for gastric cancer-specific genes based on SAGE data: melanoma inhibitory activity and matrix metalloproteinase-10 are novel prognostic factors in patients with gastric cancer. Oncogene. 2006;25:2546–2557.
    2. Aung P, Oue N, Mitani Y, Nakayama H, Yoshida K, Noguchi T, et al. Systematic search for gastric cancer-specific genes based on SAGE data: melanoma inhibitory activity and matrix metalloproteinase-10 are novel prognostic factors in patients with gastric cancer. Oncogene 2006;25:2546–57. doi:10.1038/sj.onc.1209279.
49. 1. Uekita T., Tanaka M., Takigahira M., Miyazawa Y., Nakanishi Y., Kanai Y. CUB-domain-containing protein 1 regulates peritoneal dissemination of gastric scirrhous carcinoma. Am J Pathol. 2008;172:1729–1739.
    2. Uekita T, Tanaka M, Takigahira M, Miyazawa Y, Nakanishi Y, Kanai Y, et al. CUB-domain-containing protein 1 regulates peritoneal dissemination of gastric scirrhous carcinoma. Am J Pathol 2008;172:1729–39. doi:10.2353/ajpath.2008.070981.
50. 1. Ye J., Xu J., Li Y., Huang Q., Huang J., Wang J. DDAH1 mediates gastric cancer cell invasion and metastasis via Wnt/β-catenin signaling pathway. Mol Oncol. 2017;11:1208–1224.
    2. Ye J, Xu J, Li Y, Huang Q, Huang J, Wang J, et al. DDAH1 mediates gastric cancer cell invasion and metastasis via Wnt/β-catenin signaling pathway. Mol Oncol 2017;11:1208–24. doi:10.1002/1878-0261.12089.
51. 1. Bai Z., Ye Y., Liang B., Xu F., Zhang H., Zhang Y. Proteomics-based identification of a group of apoptosis-related proteins and biomarkers in gastric cancer. Int J Oncol. 2011;38:375–383.
    2. Bai Z, Ye Y, Liang B, Xu F, Zhang H, Zhang Y, et al. Proteomics-based identification of a group of apoptosis-related proteins and biomarkers in gastric cancer. Int J Oncol 2011;38:375–83. doi:10.3892/ijo.2010.873.
52. 1. Yin Y., Liu Q., Wang B., Chen G., Xu L., Zhou H. Expression and function of heme oxygenase-1 in human gastric cancer. Exp Biol Med (Maywood) 2012;237:362–371.
    2. Yin Y, Liu Q, Wang B, Chen G, Xu L, Zhou H. Expression and function of heme oxygenase-1 in human gastric cancer. Exp Biol Med (Maywood) 2012;237:362–71. doi:10.1258/ebm.2011.011193.
53. 1. Noh S.J., Kim K.M., Jang K.Y. Individual and co-expression patterns of nerve growth factor and heme oxygenase-1 predict shorter survival of gastric carcinoma patients. Diagn Pathol. 2017;12:1–12.
    2. Noh SJ, Kim KM, Jang KY. Individual and co-expression patterns of nerve growth factor and heme oxygenase-1 predict shorter survival of gastric carcinoma patients. Diagn Pathol 2017;12:1–12. doi:10.1186/s13000-017-0644-1.
54. 1. Uekita T., Sakai R. Roles of CUB domain-containing protein 1 signaling in cancer invasion and metastasis. Cancer Sci. 2011;102:1943–1948.
    2. Uekita T, Sakai R. Roles of CUB domain-containing protein 1 signaling in cancer invasion and metastasis. Cancer Sci 2011;102:1943–8. doi:10.1111/j.1349-7006.2011.02052.x.
55. 1. Cheng S., Luo M., Ding C., Peng C., Lv Z., Tong R. Downregulation of Peptidylprolyl isomerase A promotes cell death and enhances doxorubicin-induced apoptosis in hepatocellular carcinoma. Gene. 2016;591:236–244.
    2. Cheng S, Luo M, Ding C, Peng C, Lv Z, Tong R. Downregulation of Peptidylprolyl isomerase A promotes cell death and enhances doxorubicin-induced apoptosis in hepatocellular carcinoma. Gene 2016;591:236–44. doi:10.1016/j.gene.2016.07.020.
56. 1. Ahmad A., Zeenat W., Sajad H., Shabir A., Bhat A., Ahmad M. A review on heme oxygenase-1 induction: is it a necessary evil. Inflamm Res. 2018;67:579–588.
    2. Ahmad A, Zeenat W, Sajad H, Shabir A, Bhat A, Ahmad M. A review on heme oxygenase-1 induction: is it a necessary evil. Inflamm Res 2018;67:579–88. doi:10.1007/s00011-018-1151-x.
57. 1. Sebastián V.P., Salazar G.A., Coronado-arrázola I., Schultz B.M., Vallejos O.P., Berkowitz L. vol. 9. 2018. Heme Oxygenase-1 as a modulator of intestinal inflammation development and progression; pp. 1–12.
    2. Sebastián VP, Salazar GA, Coronado-arrázola I, Schultz BM, Vallejos OP, Berkowitz L, et al. Heme Oxygenase-1 as a Modulator of Intestinal Inflammation Development and Progression 2018;9:1–12. doi:10.3389/fimmu.2018.01956.
58. 1. Yu F., Tian T., Deng B., Wang T., Qi Q., Zhu M. Multi-marker analysis of genomic annotation on gastric cancer GWAS data from Chinese populations. Gastric Cancer. 2018
    2. Yu F, Tian T, Deng B, Wang T, Qi Q, Zhu M, et al. Multi-marker analysis of genomic annotation on gastric cancer GWAS data from Chinese populations. Gastric Cancer 2018. doi:10.1007/s10120-018-0841-y.
59. 1. Wiese K.E., Walz S., Von Eyss B., Wolf E., Athineos D., Sansom O. The role of MIZ-1 in MYC-dependent tumorigenesis. Cold Spring Harb Perspect Med. 2013
    2. Wiese KE, Walz S, Von Eyss B, Wolf E, Athineos D, Sansom O, et al. The role of MIZ-1 in MYC-dependent tumorigenesis. Cold Spring Harb Perspect Med 2013. doi:10.1101/cshperspect.a014290.
60. 1. Li Y., Luo H., Liu T., Zacksenhaus E., Ben-David Y. The ets transcription factor Fli-1 in development, cancer and disease. Oncogene. 2015;34:2022–2031.
    2. Li Y, Luo H, Liu T, Zacksenhaus E, Ben-David Y. The ets transcription factor Fli-1 in development, cancer and disease. Oncogene 2015;34:2022–31. doi:10.1038/onc.2014.162.
61. 1. Kumar S., Rao N., Ge R. Emerging roles of ADAMTSS in angiogenesis and cancer. Cancers (Basel) 2012;4:1252–1299.
    2. Kumar S, Rao N, Ge R. Emerging roles of ADAMTSS in angiogenesis and cancer. Cancers (Basel) 2012;4:1252–99. doi:10.3390/cancers4041252.
62. 1. Barthel S.R., Gavino J.D., Descheny L., Dimitroff C.J. Targeting selectins and selectin ligands in inflammation and cancer. Expert Opin Ther Targets. 2007
    2. Barthel SR, Gavino JD, Descheny L, Dimitroff CJ. Targeting selectins and selectin ligands in inflammation and cancer. Expert Opin Ther Targets 2007. doi:10.1517/14728222.11.11.1473.
63. 1. Zou H., Hastie T. Regularization and variable selection via the elastic-net. J R I State Dent Soc. 2005;67:301–320.
    2. Zou H, Hastie T. Regularization and variable selection via the elastic-net. J R Stat Soc 2005;67:301–20. doi:10.1111/j.1467-9868.2005.00503.x.
64. 1. Pepe M.S., Etzioni R., Feng Z., Potter J.D., Thompson M.L., Thornquist M. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001;93:1054–1061. [11459866]
    2. Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst 2001;93:1054–61. doi:11459866.