Differences in the prognosis of gastric cancer patients of different sexes and races and the molecular mechanisms involved

Li,Huafu; Wang,Chunming; Wei,Zhewei; Chen,Wei; Guo,Zicong; He,Yulong; Zhang,Changhua

doi:10.3892/ijo.2019.4885

Differences in the prognosis of gastric cancer patients of different sexes and races and the molecular mechanisms involved

Authors:
- Huafu Li
- Chunming Wang
- Zhewei Wei
- Wei Chen
- Zicong Guo
- Yulong He
- Changhua Zhang
View Affiliations

Affiliations: Digestive Medicine Center, The Seventh Affiliated Hospital of Sun Yat‑Sen University, Shenzhen, Guangdong 518107, P.R. China, Department of Gastrointestinopancreatic Surgery, The First Affiliated Hospital of Sun Yat‑Sen University, Guangzhou, Guangdong 510080, P.R. China
Published online on: September 26, 2019 https://doi.org/10.3892/ijo.2019.4885
Pages: 1049-1068
Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

To evaluate the prognostic and molecular mechanisms of sex and racial differences in gastric cancer, data from two large centers were used to retrospectively analyze the survival of gastric cancer patients with regard to sex and racial differences. In examining the molecular mechanism of sex in gastric cancer patients of different races, data from The Cancer Genome Atlas database were used to analyze differentially expressed genes (DEGs), and Gene Ontology (GO) enrichment and DNA methylation analyses were performed. Among White gastric cancer patients, it was found that the survival prognosis for females was better than that for males; conversely, among Chinese patients, males had a better prognosis. For African Americans, sex may have an impact on gastric cancer, but this relationship was unclear. The core DEGs between the different sexes included glycogenin 2 pseudogene 1, ribosomal protein S4 Y‑linked 1, taxilin‑γ and eukaryotic translation initiation factor 1A X‑linked among White patients, and GO enrichment analysis revealed that these genes act mainly through RNA binding and transcription pathways. Among Black patients, core DEGs included DnaJ heat shock protein family (Hsp40) member C5, histone deacetylase 10, neogenin 1 and SMG5 nonsense mediated mRNA decay factor, which are mainly related to pathways of cellular structural changes based on GO enrichment analysis. For Asian patients, core DEGs included zinc finger protein Y‑linked, thymosin β4 Y‑linked, zinc finger protein 787 and ubiquitously transcribed tetratricopeptide repeat containing, Y‑linked, participating in cell surface receptor‑associated signal transduction and G‑protein coupled receptor protein signaling pathways, according to GO. The expression of different core genes and differences in pathways are likely to be the main causes affecting the variation observed among gastric cancer patients of different races and sexes.

View Figures

View References

1	Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, Bonaventure A, Valkov M, Johnson CJ, Estève J, 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. 391:1023–1075. 2018. View Article : Google Scholar : PubMed/NCBI
2	Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI
3	Dong E, Duan L and Wu BU: Racial and Ethnic minorities at increased risk for gastric cancer in a regional US population study. Clin Gastroenterol Hepatol. 15:511–517. 2017. View Article : Google Scholar
4	Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D and Bray F: Cancer incidence and mortality worldwide: Sources methods and major patterns in GLOBOCAN 2012. Int J Cancer. 136:E359–E386. 2015. View Article : Google Scholar
5	Karimi P, Islami F, Anandasabapathy S, Freedman ND and Kamangar F: Gastric cancer: Descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomarkers Prev. 23:700–713. 2014. View Article : Google Scholar : PubMed/NCBI
6	Hwang KT, Kim J, Jung J, Chang JH, Chai YJ, Oh SW, Oh S, Kim YA, Park SB and Hwang KR: Impact of breast cancer subtypes on prognosis of women with operable invasive breast cancer: A population-based study using SEER database. Clin Cancer Res. 25:1970–1979. 2019.
7	Greene FL, Compton CC, Fritz AG, Shah JP and Winchester DP: AJCC Cancer Staging Atlas. Springer; New York, NY: 2006, View Article : Google Scholar
8	Li B and Dewey CN: RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 12:3232011. View Article : Google Scholar : PubMed/NCBI
9	Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD and Irizarry RA: Minfi: A flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics. 30:1363–1369. 2014. View Article : Google Scholar : PubMed/NCBI
10	Sandoval J, Mendez-Gonzalez J, Nadal E, Chen G, Carmona FJ, Sayols S, Moran S, Heyn H, Vizoso M, Gomez A, et al: A prog nostic DNA methylation signature for stage I non-small-cell lung cancer. J Clin Oncol. 31:4140–4147. 2013. View Article : Google Scholar : PubMed/NCBI
11	Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D and Beck S: A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 29:189–196. 2013. View Article : Google Scholar :
12	Langevin SM, Butler RA, Eliot M, Pawlita M, Maccani JZ, McClean MD and Kelsey KT: Novel DNA methylation targets in oral rinse samples predict survival of patients with oral squamous cell carcinoma. Oral Oncol. 50:1072–1080. 2014. View Article : Google Scholar : PubMed/NCBI
13	Böhnke JR: Explanation in causal inference: Methods for mediation and interaction. Q J Exp Psychol (Hove). 69:1243–1244. 2016. View Article : Google Scholar
14	Gene Ontology Consortium: The gene ontology (GO) project in 2006. Nucleic Acids Res. 34:D322–D326. 2006. View Article : Google Scholar :
15	Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
16	Györffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q and Szallasi Z: An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 123:725–731. 2010. View Article : Google Scholar
17	Austin PC: Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 10:150–161. 2011. View Article : Google Scholar :
18	Herman JG and Baylin SB: Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med. 349:2042–2054. 2003. View Article : Google Scholar : PubMed/NCBI
19	Aron M, Nguyen MM, Stein RJ and Gill IS: Impact of gender in renal cell carcinoma: An analysis of the SEER database. Eur Urol. 54:133–140. 2008. View Article : Google Scholar
20	Kim HW, Kim JH, Lim BJ, Kim H, Kim H, Park JJ, Youn YH, Park H, Noh SH, Kim JW and Choi SH: Sex disparity in gastric cancer: Female sex is a poor prognostic factor for advanced gastric cancer. Ann Surg Oncol. 23:4344–4351. 2016. View Article : Google Scholar : PubMed/NCBI
21	Li H, Wei Z, Wang C, Chen W, He Y and Zhang C: Gender differences in gastric cancer survival: 99,922 cases based on the SEER database. J Gastrointest Surg. Jul 25–2019.Epub ahead of print. View Article : Google Scholar
22	Ashktorab H, Kupfer SS, Brim H and Carethers JM: Racial disparity in gastrointestinal cancer risk. Gastroenterology. 153:910–923. 2017. View Article : Google Scholar : PubMed/NCBI
23	Loh M, Koh KX, Yeo BH, Song CM, Chia KS, Zhu F, Yeoh KG, Hill J, Iacopetta B and Soong R: Meta-analysis of genetic polymorphisms and gastric cancer risk: Variability in associations according to race. Eur J Cancer. 45:2562–2568. 2009. View Article : Google Scholar : PubMed/NCBI
24	Lou W, Ding B and Fan W: High expression of pseudogene PTTG3P indicates a poor prognosis in human breast cancer. Mol Ther Oncolytics. 14:15–26. 2019. View Article : Google Scholar : PubMed/NCBI
25	Jung Y, Lee S, Choi HS, Kim SN, Lee E, Shin Y, Seo J, Kim B, Jung Y, Kim WK, et al: Clinical validation of colorectal cancer biomarkers identified from bioinformatics analysis of public expression data. Clin Cancer Res. 17:700–709. 2011. View Article : Google Scholar : PubMed/NCBI
26	Yu VW, Ambartsoumian G, Verlinden L, Moir JM, Prud'homme J, Gauthier C, Roughley PJ and St-Arnaud R: FIAT represses ATF4-mediated transcription to regulate bone mass in transgenic mice. J Cell Biol. 169:591–601. 2005. View Article : Google Scholar : PubMed/NCBI
27	Benitez BA and Sands MS: Primary fibroblasts from CSPα mutation carriers recapitulate hallmarks of the adult onset neuronal ceroid lipofuscinosis. Sci Rep. 7:63322017. View Article : Google Scholar
28	Tao X, Yan Y, Lu L and Chen B: HDAC10 expression is associated with DNA mismatch repair gene and is a predictor of good prognosis in colon carcinoma. Oncol Lett. 14:4923–4929. 2017. View Article : Google Scholar : PubMed/NCBI
29	Qu H, Sun H and Wang X: Neogenin-1 promotes cell proliferation, motility, and adhesion by up-regulation of zinc finger E-box binding homeobox 1 via activating the Rac1/PI3K/AKT pathway in gastric cancer cells. Cell Physiol Biochem. 48:1457–1467. 2018. View Article : Google Scholar : PubMed/NCBI
30	Wong HY, Wang GM, Croessmann S, Zabransky DJ, Chu D, Garay JP, Cidado J, Cochran RL, Beaver JA, Aggarwal A, et al: TMSB4Y is a candidate tumor suppressor on the Y chromosome and is deleted in male breast cancer. Oncotarget. 6:44927–44940. 2015. View Article : Google Scholar : PubMed/NCBI
31	Tricoli JV and Bracken RB: ZFY gene expression and retention in human prostate adenocarcinoma. Genes Chromosomes Cancer. 6:65–72. 1993. View Article : Google Scholar : PubMed/NCBI
32	Li Y, Tan BB, Zhao Q, Fan LQ, Wang D and Liu Y: ZNF139 promotes tumor metastasis by increasing migration and invasion in human gastric cancer cells. Neoplasma. 61:291–298. 2014. View Article : Google Scholar : PubMed/NCBI