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SAMD5 mRNA was overexpressed in prostate cancer and can predict biochemical recurrence after radical prostatectomy

  • Fei Li
  • Yong Xu
  • Ran-Lu LiuEmail author
Urology - Original Paper
  • 5 Downloads

Abstract

Purpose

To identify a novel biomarker that can predict biochemical recurrence (BCR) after radical prostatectomy.

Methods

The gene expression profile of SAMD5 in prostate cancer was explored based on the oncomine database and The Cancer Genomic Atlas (TCGA). The follow-up information and clinical pathologic variables were extracted from the following cohort study: TCGA_prostate carcinoma. And then, survival analysis was conducted using the Kaplan–Meier plot and Cox’s proportional hazard regression model. Furthermore, another independent cohort study: Taylor prostate, was also acquired to validate the predictive effect of SAMD5 on BCR. In addition, the expression profile of SAMD5 in other cancer types was investigated using TCGA dataset.

Results

SAMD5 mRNA was shown to be up-regulated in multiple microarray datasets of prostate cancer with the strict statistic criteria: p < 0.01 and fold change ≥ 2. In TCGA_PCa cohort study, high expression of SAMD5 was a risk factor for patients on post-operative BCR (HR 2.181, 95%CI 1.199–3.966, p = 0.011) and this predictive ability was independent of Gleason score and pathologic T stage (HR 2.018, 95%CI 1.102–3.698, p = 0.023). In another validating cohort study, the statistic trend was similar, and the pooled analysis by combining the two cohort study further confirmed its prognostic effect.

Conclusion

SAMD5 mRNA was overexpressed in prostate cancer and had powerful prognostic ability on predicting post-operative BCR, independent of Gleason score and pathologic T stage. Its high expression was associated with poor prognosis after RP.

Keywords

SAMD5 Biomarker Recurrence Prognosis Prostatic neoplasms 

Abbreviations

BCR

Biochemical recurrence

RP

Radical prostatectomy

TCGA

The Cancer Genomic Atlas

GEPIA

Gene expression profiling interactive analysis

TPM

Transcripts per million

AJCC

American Joint Committee on Cancer

Notes

Acknowledgements

This research was based on public database: TCGA and GEPIA, and we are grateful for the extraordinary works of these project groups. We thank Bioinformatics Engineer Rang-Fei Zhu for his excellent pretreatment of TCGA-PCa data.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

The patients’ information involved in our research were obtained from The Cancer Genome Atlas (TCGA) and Taylor Prostate dataset. All the patients and treatments complied with the principles laid down in the Declaration of Helsinki in 1964 and its later amendments or comparable ethical standards.

Informed consent

Informed consent was confirmed by all the patients participated in the TCGA-Prostate adenocarcinoma project and Taylor prostate project.

Supplementary material

11255_2019_2096_MOESM1_ESM.tif (6.8 mb)
Supplementary material 1 Fig. S1 SAMD5 protein expression in Prostate cancer. a. The HPA research group detected SAMD5 protein expression in 10 prostate cancer samples via IHC technology, and found that 6 samples were stained with Low to medium strength. b. SAMD5 protein was shown to be expressed in prostate cancer cells (Brown dots) and located in cytoplasm or membrane. c. SAMD5 protein staining was negative in normal prostate tissue. (TIF 6927 KB)
11255_2019_2096_MOESM2_ESM.eps (1.7 mb)
Supplementary material 2 Fig. S2 Sensitive analysis of pooled HRs. a. The pooled HR calculated from univariable Cox regression model in two datasets was stable. After removing a specific study and calculating the pooled HR of the remaining study, both the point estimations dropped into the interval:1.25-3.09. b. The pooled HR calculated from multivariable Cox regression model in two datasets was stable. After removing a specific study and calculating the pooled HR of the remaining study, both the point estimations dropped into the interval:1.09-2.77. (EPS 1780 KB)

References

  1. 1.
    Bray F, Ferlay J, Soerjomataram I et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424.  https://doi.org/10.3322/caac.21492 CrossRefGoogle Scholar
  2. 2.
    Cornford P, Bellmunt J, Bolla M et al (2017) EAU-ESTRO-SIOG guidelines on prostate cancer. Part II: treatment of relapsing, metastatic, and castration-resistant prostate cancer. Eur Urol 71:630–642.  https://doi.org/10.1016/j.eururo.2016.08.002 CrossRefGoogle Scholar
  3. 3.
    Li D, Lv H, Hao X et al (2018) Prognostic value of serum alkaline phosphatase in the survival of prostate cancer: evidence from a meta-analysis. Cancer Manage Res 10:3125–3139.  https://doi.org/10.2147/CMAR.S174237 CrossRefGoogle Scholar
  4. 4.
    Uhlen M, Zhang C, Lee S et al (2017) A pathology atlas of the human cancer transcriptome. Science.  https://doi.org/10.1126/science.1260419 Google Scholar
  5. 5.
    Wang Y, Shang Y, Li J et al (2018) Specific Eph receptor-cytoplasmic effector signaling mediated by SAM-SAM domain interactions. elife.  https://doi.org/10.7554/eLife.35677 Google Scholar
  6. 6.
    Matsuo T, Dat le T, Komatsu M et al (2014) Early growth response 4 is involved in cell proliferation of small cell lung cancer through transcriptional activation of its downstream genes. PLoS ONE 9:e113606.  https://doi.org/10.1371/journal.pone.0113606 CrossRefGoogle Scholar
  7. 7.
    Watanabe T, Kobunai T, Akiyoshi T et al (2014) Prediction of response to preoperative chemoradiotherapy in rectal cancer by using reverse transcriptase polymerase chain reaction analysis of four genes. Dis Colon Rectum 57:23–31.  https://doi.org/10.1097/01.dcr.0000437688.33795.9d CrossRefGoogle Scholar
  8. 8.
    The Cancer Genome Atlas Research Network (2015) The molecular taxonomy of primary prostate cancer. Cell 163:1011–1025.  https://doi.org/10.1016/j.cell.2015.10.025 CrossRefGoogle Scholar
  9. 9.
    Taylor BS, Schultz N, Hieronymus H et al (2010) Integrative genomic profiling of human prostate cancer. Cancer Cell 18:11–22.  https://doi.org/10.1016/j.ccr.2010.05.026 CrossRefGoogle Scholar
  10. 10.
    Rhodes DR, Yu J, Shanker K et al (2004) ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6:1–6.  https://doi.org/10.1016/S1476-5586(04)80047-2 CrossRefGoogle Scholar
  11. 11.
    Tang Z, Li C, Kang B et al (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45:W98–W102.  https://doi.org/10.1093/nar/gkx247 CrossRefGoogle Scholar
  12. 12.
    Li B, Ruotti V, Stewart RM et al (2010) RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26:493–500.  https://doi.org/10.1093/bioinformatics/btp692 CrossRefGoogle Scholar
  13. 13.
    Thompson IM, Andriole GL, Blumenstein B et al (2003) AJCC cancer staging manual, 6th edn. Springer, New YorkGoogle Scholar
  14. 14.
    Cerami E, Gao J, Dogrusoz U et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–404.  https://doi.org/10.1158/2159-8290.CD-12-0095 CrossRefGoogle Scholar
  15. 15.
    Gao J, Aksoy BA, Dogrusoz U et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:pl1.  https://doi.org/10.1126/scisignal.2004088 CrossRefGoogle Scholar
  16. 16.
    Tripathi S, Pohl MO, Zhou Y et al (2015) Meta- and Orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virus budding. Cell Host Microbe 18:723–735.  https://doi.org/10.1016/j.chom.2015.11.002 CrossRefGoogle Scholar
  17. 17.
    Tomlins SA, Mehra R, Rhodes DR et al (2007) Integrative molecular concept modeling of prostate cancer progression. Nat Genet 39:41–51.  https://doi.org/10.1038/ng1935 CrossRefGoogle Scholar
  18. 18.
    Varambally S, Yu J, Laxman B et al (2005) Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. Cancer Cell 8:393–406.  https://doi.org/10.1016/j.ccr.2005.10.001 CrossRefGoogle Scholar
  19. 19.
    Lapointe J, Li C, Higgins JP et al (2004) Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA 101:811–816.  https://doi.org/10.1073/pnas.0304146101 CrossRefGoogle Scholar
  20. 20.
    Arredouani MS1, Lu B, Bhasin M et al (2009) Identification of the transcription factor single-minded homologue 2 as a potential biomarker and immunotherapy target in prostate cancer. Clin Cancer Res 15:5794–5802.  https://doi.org/10.1158/1078-0432.CCR-09-0911 CrossRefGoogle Scholar
  21. 21.
    Grasso CS, Wu YM, Robinson DR et al (2012) The mutational landscape of lethal castration-resistant prostate cancer. Nature 487:239–243.  https://doi.org/10.1038/nature11125 CrossRefGoogle Scholar
  22. 22.
    Luo JH, Yu YP, Cieply K et al (2002) Gene expression analysis of prostate cancers. Mol Carcinog 33:25–35.  https://doi.org/10.1002/mc.10018 CrossRefGoogle Scholar
  23. 23.
    Yagai T, Matsui S, Harada K et al (2017) Expression and localization of sterile alpha motif domain containing 5 is associated with cell type and malignancy of biliary tree. PLoS ONE 12:e0175355.  https://doi.org/10.1371/journal.pone.0175355 CrossRefGoogle Scholar
  24. 24.
    Lemmon MA, Schlessinger J (2010) Cell signaling by receptor tyrosine kinases. Cell 141:1117–1134.  https://doi.org/10.1016/j.cell.2010.06.011 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Urology, National Key Clinical Specialty of UrologyThe Second Hospital of Tianjin Medical UniversityTianjinChina
  2. 2.Department of UrologyRugao City People’s HospitalRugao CityChina

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