Skip to main content

Advertisement

Log in

Transcriptomic profiling on localized gastric cancer identified CPLX1 as a gene promoting malignant phenotype of gastric cancer and a predictor of recurrence after surgery and subsequent chemotherapy

  • Original Article—Alimentary Tract
  • Published:
Journal of Gastroenterology Aims and scope Submit manuscript

Abstract

Background

Localized gastric cancer (GC) becomes fatal once recurring. We still have room for improving their prognoses.

Methods

Transcriptomic analysis was done on surgically resected specimens of 16 patients with UICC stage III GC who underwent curative gastrectomy and adjuvant oral fluoropyrimidine monotherapy. Four of them were free from disease for longer than 5 years, and the others experienced metachronous metastasis within 2 years after surgery. Quantitative RT-PCR determined mRNA expression levels of primary gastric cancer tissues, which were collected from 180 patients who underwent gastric resection for stage II–III GC without preoperative treatment between 2001 and 2014. We tested alteration of malignant phenotypes including drug resistance of GC cell lines by siRNA and shRNA-mediated knockdown and forced expression experiments.

Results

CPLX1 was identified as a candidate biomarker for GC recurrence among 57,749 genes. Inhibiting and forced expression experiments indicated that CPLX1 promotes proliferation, motility, and invasiveness of GC cells, and decreases apoptosis and sensitivity to fluorouracil. Subcutaneous xenograft mouse models revealed that shRNA-mediated knockdown of CPLX1 also attenuated tumor growth of MKN1 cells in vivo. Overexpression of CPLX1 in gastric cancer tissue correlated with worse prognosis and was an independent risk factor for peritoneal recurrence in subgroups receiving adjuvant chemotherapy.

Conclusions

CPLX1 may represent a biomarker for recurrence of gastric cancer and a target for therapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

EMT:

Epithelial–mesenchymal transition

mRNA:

Messenger RNA

OD:

Optical density

qRT-PCR:

Quantitative reverse-transcription polymerase chain reaction

RNA-seq:

RNA-sequencing

siRNA:

Small interfering RNA

shRNA:

Small hairpin RNA

VC:

Vesicular cycle

References

  1. Allemani C, Matsuda T, Di Carlo V, et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018;391:1023–75.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ferlay J, Colombet M, Soerjomataram I, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144:1941–53.

    Article  CAS  PubMed  Google Scholar 

  3. Hiki N, Katai H, Mizusawa J, et al. Long-term outcomes of laparoscopy-assisted distal gastrectomy with suprapancreatic nodal dissection for clinical stage I gastric cancer: a multicenter phase II trial (JCOG0703). Gastric Cancer. 2018;21:155–61.

    Article  PubMed  Google Scholar 

  4. Shimizu D, Kanda M, Kodera Y, et al. Cutting-edge evidence of adjuvant treatments for gastric cancer. Expert Rev Gastroenterol Hepatol. 2018;12:1109–22.

    Article  CAS  PubMed  Google Scholar 

  5. Knight G, Earle CC, Cosby R, et al. Neoadjuvant or adjuvant therapy for resectable gastric cancer: a systematic review and practice guideline for North America. Gastric Cancer. 2013;16(1):28–40.

    Article  CAS  PubMed  Google Scholar 

  6. Pelcovits A, Almhanna K. Locoregional gastric cancer: a narrative review of multidisciplinary management. Ann Transl Med. 2020;8:1108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gupta GP, Massague J. Cancer metastasis: building a framework. Cell. 2006;127:679–95.

    Article  CAS  PubMed  Google Scholar 

  8. Tolios A, De Las RJ, Hovig E, et al. Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions. Drug Resist Updat. 2020;48: 100662.

    Article  CAS  PubMed  Google Scholar 

  9. Tan P, Yeoh KG. Genetics and molecular pathogenesis of gastric adenocarcinoma. Gastroenterology. 2015;149(1153–62): e3.

    Google Scholar 

  10. Brosnan JA, Iacobuzio-Donahue CA. A new branch on the tree: next-generation sequencing in the study of cancer evolution. Semin Cell Dev Biol. 2012;23:237–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Redler S, Strom TM, Wieland T, et al. Variants in CPLX1 in two families with autosomal-recessive severe infantile myoclonic epilepsy and ID. Eur J Hum Genet. 2017;25:889–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nagy Á, Lánczky A, Menyhárt O, et al. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep. 2018;8:9227.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Tanaka H, Kanda M, Miwa T, et al. Pattern-specific transcriptomics identifies ASGR2 as a predictor of hematogenous recurrence of gastric cancer. Mol Cancer Res. 2018;16:1420–9.

    Article  CAS  PubMed  Google Scholar 

  14. Tang M, Sun J, Shimizu K, et al. Evaluation of methods for differential expression analysis on multi-group RNA-seq count data. BMC Bioinform. 2015;16:361.

    Article  Google Scholar 

  15. Tanaka H, Kanda M, Shimizu D, et al. FAM46C serves as a predictor of hepatic recurrence in patients with resectable gastric cancer. Ann Surg Oncol. 2016;24:3438–45.

    Article  PubMed  Google Scholar 

  16. Tanaka HKM, Miwa T, Umeda S, Sawaki K, Tanaka C, Kobayashi D, Hayashi M, Yamada S, Nakayama G, Koike M, Kodera Y. G protein subunit gamma 4 expression has potential for detection, prediction, and therapeutic targeting in liver metastasis of gastric cancer. Br J Cancer. 2021;125:220–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kanda M, Tanaka H, Shimizu D, et al. SYT7 acts as a driver of hepatic metastasis formation of gastric cancer cells. Oncogene. 2018;37:5355–66.

    Article  CAS  PubMed  Google Scholar 

  18. Miwa T, Kanda M, Shimizu D, et al. Hepatic metastasis of gastric cancer is associated with enhanced expression of ethanolamine kinase 2 via the p53-Bcl-2 intrinsic apoptosis pathway. Br J Cancer. 2021;124:1449–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kanda M, Shimizu D, Tanaka H, et al. Synaptotagmin XIII expression and peritoneal metastasis in gastric cancer. Br J Surg. 2018;105:1349–58.

    Article  CAS  PubMed  Google Scholar 

  20. Kanda M, Shimizu D, Tanaka H, et al. Significance of SYT8 for the detection, prediction, and treatment of peritoneal metastasis from gastric cancer. Ann Surg. 2016;267:495–503.

    Article  Google Scholar 

  21. Hafner M, Niepel M, Chung M, et al. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods. 2016;13:521–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kanda M, Shimizu D, Sawaki K, et al. Therapeutic monoclonal antibody targeting of neuronal pentraxin receptor to control metastasis in gastric cancer. Mol Cancer. 2020;19:131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Umeda S, Kanda M, Miwa T, et al. Fraser extracellular matrix complex subunit 1 promotes liver metastasis of gastric cancer. Int J Cancer. 2020;146:2865–76.

    Article  CAS  PubMed  Google Scholar 

  24. Sharma S, Ghufran SM, Ghose S, et al. Cytoplasmic vacuolation with endoplasmic reticulum stress directs sorafenib induced non-apoptotic cell death in hepatic stellate cells. Sci Rep. 2021;11:3089.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kwon OH, Park JL, Baek SJ, et al. Aberrant upregulation of ASCL2 by promoter demethylation promotes the growth and resistance to 5-fluorouracil of gastric cancer cells. Cancer Sci. 2013;104:391–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rizo J, Rosenmund C. Synaptic vesicle fusion. Nat Struct Mol Biol. 2008;15(7):665–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Südhof TC, Rothman JE. Membrane fusion: grappling with SNARE and SM proteins. Science. 2009;323:474–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Mohrmann R, Dhara M, Bruns D. Complexins: small but capable. Cell Mol Life Sci. 2015;72:4221–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kasai H, Takahashi N, Tokumaru H. Distinct initial SNARE configurations underlying the diversity of exocytosis. Physiol Rev. 2012;92:1915–64.

    Article  CAS  PubMed  Google Scholar 

  30. Lawson MA, Maxfield FR. Ca(2+)- and calcineurin-dependent recycling of an integrin to the front of migrating neutrophils. Nature. 1995;377:75–9.

    Article  CAS  PubMed  Google Scholar 

  31. Proux-Gillardeaux V, Gavard J, Irinopoulou T, et al. Tetanus neurotoxin-mediated cleavage of cellubrevin impairs epithelial cell migration and integrin-dependent cell adhesion. Proc Natl Acad Sci U S A. 2005;102:6362–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pavarotti MA, Tokarz V, Frendo-Cumbo S, et al. Complexin-2 redistributes to the membrane of muscle cells in response to insulin and contributes to GLUT4 translocation. Biochem J. 2021. https://doi.org/10.1042/bcj20200542.

    Article  PubMed  Google Scholar 

  33. Desiniotis A, Kyprianou N. Significance of talin in cancer progression and metastasis. Int Rev Cell Mol Biol. 2011;289:117–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kim J, Jeong H, Lee Y, et al. HRG-β1-driven ErbB3 signaling induces epithelial-mesenchymal transition in breast cancer cells. BMC Cancer. 2013;13:383.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yilmaz M, Christofori G. EMT, the cytoskeleton, and cancer cell invasion. Cancer Metastasis Rev. 2009;28:15–33.

    Article  PubMed  Google Scholar 

  36. Wu C, Ding H, Wang S, et al. DAXX inhibits cancer stemness and epithelial-mesenchymal transition in gastric cancer. Br J Cancer. 2020;122:1477–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yu L, Peng F, Dong X, et al. Sex-determining region Y chromosome-related high-mobility-group box 10 in cancer: a potential therapeutic target. Front Cell Dev Biol. 2020;8: 564740.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Vander Haar E, Lee SI, Bandhakavi S, et al. Insulin signalling to mTOR mediated by the Akt/PKB substrate PRAS40. Nat Cell Biol. 2007;9:316–23.

    Article  CAS  PubMed  Google Scholar 

  39. Figueiredo VC, Markworth JF, Cameron-Smith D. Considerations on mTOR regulation at serine 2448: implications for muscle metabolism studies. Cell Mol Life Sci. 2017;74:2537–45.

    Article  CAS  PubMed  Google Scholar 

  40. Mehta SL, Manhas N, Raghubir R. Molecular targets in cerebral ischemia for developing novel therapeutics. Brain Res Rev. 2007;54:34–66.

    Article  CAS  PubMed  Google Scholar 

  41. Sasako M, Sakuramoto S, Katai H, et al. Five-year outcomes of a randomized phase III trial comparing adjuvant chemotherapy with S-1 versus surgery alone in stage II or III gastric cancer. J Clin Oncol. 2011;29:4387–93.

    Article  CAS  PubMed  Google Scholar 

  42. Noh SH, Park SR, Yang HK, et al. Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): 5-year follow-up of an open-label, randomised phase 3 trial. Lancet Oncol. 2014;15:1389–96.

    Article  CAS  PubMed  Google Scholar 

  43. Kanda M, Suh YS, Park DJ, et al. Serum levels of ANOS1 serve as a diagnostic biomarker of gastric cancer: a prospective multicenter observational study. Gastric Cancer. 2020;23:203–11.

    Article  PubMed  Google Scholar 

  44. Terashima M, Yoshikawa T, Boku N, et al. Current status of perioperative chemotherapy for locally advanced gastric cancer and JCOG perspectives. Jpn J Clin Oncol. 2020;50:528–34.

    Article  PubMed  Google Scholar 

  45. Yoshida K, Kodera Y, Kochi M, et al. Addition of docetaxel to oral fluoropyrimidine improves efficacy in patients with stage iii gastric cancer: interim analysis of JACCRO GC-07, a randomized controlled trial. J Clin Oncol. 2019;37:1296–304.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Taiho Pharmaceutical Co. Ltd for technical support, and advice on this project through discussion on our result.

Funding

This study was funded by Taiho Pharmaceutical Co. Ltd.

Author information

Authors and Affiliations

Authors

Contributions

Study concept and design; MK and YK. Acquisition of data; MK, HT, DS, and YK. Analysis and interpretation of data; HT, MK, and YK. Drafting of the manuscript; HT, MK, and YK. Critical revision of the manuscript for important intellectual content; HT, MK, SD, CT, NH, YI, MH, GN, and KY. Statistical analysis; HT and MK. Obtained funding; MK and YK. Technical, or material support; HT, MK, DS, and CT. Study supervision; YK.

Corresponding author

Correspondence to Mitsuro Kanda.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Brief description: Transcriptomic analysis identified CPLX1 gene as a novel oncogene candidate for gastric cancer. CPLX1 may promote epithelial–mesenchymal transition and evading apoptosis of gastric cancer cells even under a cytotoxic agent, and also be a predictor for recurrence after surgery for UICC Stage II–III gastric cancer.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 21 KB)

Supplementary file2 (DOCX 17 KB)

Supplementary file3 (DOCX 18 KB)

535_2022_1884_MOESM4_ESM.pdf

Supplementary file4 Supplemental Fig. S1. Kaplan–Meier plot panel of overall survival, and progression-free survival using external datasets grouped by the median value of each gene expression level to extract 12 candidate genes up-regulated. The left panels present overall survival and the right present recurrence-free survival. Data were retrieved from the GSE62254, GSE15459, GSE22377, GSE62254, GSE29272, and GSE38749, of which UICC Stages were limited to II–III. Supplemental Fig. S2. (a) Conventional immunoblot assay detecting CPLX1 expression of KATOIII cells. (b) Whole picture of digital immunoblot of seven gastric cancer cells (two left panels) and (c) of cells transfected with siRNAs and overexpression vector compared with control siRNA and empty vector, respectively. (d) CPLX1-knockdown efficiencies of N87, OCUM1, and NUGC2 cells. (e) Proliferation assay comparing KATOIII cells, KATOIII cells transfected with siControl, and siCPLX1, and comparing NUGC2 cells transfected with siControl and siCPLX1. Supplemental Fig. S3. (a) Apoptotic cell detection by staining with annexin-V (See Fig. 2e). Phase-contrast and fluorescence images were merged to be presented. (b) Proportions of annexin-V positive stained KATOIII cells untreated, and transfected with siControl and siCPLX1. (c) Drug sensitivity tests to fluorouracil (5-FU) on seven gastric cancer (GC) cell lines. GR values indicate values calculated by normalized growth rate inhibition (GR) metrics. (d) Scatter plot between CPLX1 mRNA expression (CPLX1/GAPDH) and area under the dose–response curves (AUC) to 5-FU of seven GC cell lines. A correlation was tested with the Spearman test. (e) PCR-based EMT-related 84 genes profiling to test co-expression with CPLX1 among 14 GC cell lines. The significances of coefficients were represented by rho (ρ) values. In this panel, other genes generally considered to be related to epithelial–mesenchymal transition (EMT) were presented than the genes presented picked out in Fig. 3. (f) Whole raw pictures of the sandwich ELISA array comparing parental KATOIII cells, that mediated by siControl, and siCPLX1. (g) The whole picture of digital immunoblot of MKN1cells transfected with shCPLX1 compared with the control shRNA (shControl). Supplemental Fig. S4. (a) Histogram of CPLX1 mRNA expression values of 180 samples. The upper quantile value of the present cohort was 0.00682 and indicated by the vertical line. (b) Representative immunohistochemistry of gastric cancer tissue with weak CPLX1 expression and adjacent gastric tissue. (PDF 59442 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tanaka, H., Kanda, M., Shimizu, D. et al. Transcriptomic profiling on localized gastric cancer identified CPLX1 as a gene promoting malignant phenotype of gastric cancer and a predictor of recurrence after surgery and subsequent chemotherapy. J Gastroenterol 57, 640–653 (2022). https://doi.org/10.1007/s00535-022-01884-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00535-022-01884-6

Keywords

Navigation