Identification of novel biomarkers with potential for diagnosis and prognosis of gastric cancer: a Bioinformatics Approach

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Marcos Vinicius Rossetto
Fernanda Pessi de Abreu
Pedro Lenz Casa
Ivaine Tais Sauthier Sartor
Scheila de Avila e Silva


Introduction: Gastric cancer (GC) is the fifth most diagnosed neoplasia and the third leading cause of cancer-related deaths. A substantial number of patients exhibit an advanced GC stage once diagnosed. Therefore, the search for biomarkers contributes to the improvement and development of therapies. Objective: This study aimed to identify potential GC biomarkers making use of in silico tools. Methods: Gastric tissue microarray data available in Gene Expression Omnibus and The Cancer Genome Atlas Program was extracted. We applied statistical tests in the search for differentially expressed genes between tumoral and non-tumoral adjacent tissue samples. The selected genes were submitted to an in-house tool for analyses of functional enrichment, survival rate, histological and molecular classifications, and clinical follow-up data. A decision tree analysis was performed to evaluate the predictive power of the potential biomarkers. Results: In total, 39 differentially expressed genes were found, mostly involved in extracellular structure organization, extracellular matrix organization, and angiogenesis. The genes SLC7A8, LY6E, and SIDT2 showed potential as diagnostic biomarkers considering the differential expression results coupled with the high predictive power of the decision tree models. Moreover, GC samples showed lower SLC7A8 and SIDT2 expression, whereas LY6E was higher. SIDT2 demonstrated a potential prognostic role for the diffuse type of GC, given the higher patient survival rate for lower gene expression. Conclusion: Our study outlines novel biomarkers for GC that may have a key role in tumor progression. Nevertheless, complementary in vitro analyses are still needed to further support their potential.


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Rossetto, M. V., Abreu, F. P. de, Casa, P. L., Sartor, I. T. S., & Silva, S. de A. e. (2023). Identification of novel biomarkers with potential for diagnosis and prognosis of gastric cancer: a Bioinformatics Approach. ABCS Health Sciences, 48, e023227.
Original Articles


Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424.

Luo G, Zhang Y, Guo P, Wang L, Huang Y, Li K. Global patterns, and trends in stomach cancer incidence: Age, period and birth cohort analysis. Int J Cancer. 2017;141(7):1333-44.

Gao J, Lindsay J, Watt S, Bahceci I, Lukasse P, Abeshouse A, et al. The cBioPortal for cancer genomics and its application in precision oncology. Cancer Res. 2016;76(14 Suppl):5277.

Kelley JR, Duggan JM. Gastric cancer epidemiology and risk factors. J Clin Epidemiol. 2003;56(1):1-9.

Choli-Papadopoulou T, Kottakis F, Papadopoulos G, Pendas S. Helicobacter pylori neutrophil activating protein as target for new drugs against H. pylori inflammation. World J Gastroenterol. 2011;17(21):2585-91.

Flora S, La Maestra S. Epidemiology of cancers of infectious origin and prevention strategies. J Prev Med Hyg. 2015;56(1):E15-20.

Bornschein J, Kandulski A, Selgrad M, Malfertheiner P. From gastric inflammation to gastric cancer. Dig Dis. 2010;28(4-5):609-14.

Schmidt N, Peitz U, Lippert H, Malfertheiner P. Missing gastric cancer in dyspepsia. Aliment Pharmacol Ther. 2005;21(7):813-20.

Lee JY, Kim HI, Kim YN, Hong JH, Alshomimi S, An JY, et al. Clinical significance of the prognostic nutritional index for predicting short- and long-term surgical outcomes after gastrectomy: a retrospective analysis of 7781 gastric cancer patients. Medicine (Baltimore). 2016;95(18):e3539.

Henry NL, Hayes DF. Cancer biomarkers. Mol Oncol. 2012;6(2):140-6.

Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286(5439):531-7.

Wan C, Li J. Synthesis of well-dispersed magnetic CoFe2O4 nanoparticles in cellulose aerogels via a facile oxidative co-precipitation method. Carbohydr Polym. 2015;134:144-50.

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(5):449-56.

Broadhead ML, Clark JCM, Dass CR, Choong PFM. Microarray: an instrument for cancer surgeons of the future? ANZ J Surg. 2010;80(7-8):531-6.

D’Angelo G, Di Rienzo T, Ojetti V. Microarray analysis in gastric cancer: a review. World J Gastroenterol. 2014;20(34):11972-6.

Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207-10.

Athar A, Füllgrabe A, George N, Iqbal H, Huerta L, Ali A, et al. ArrayExpress update - from bulk to single-cell expression data. Nucleic Acids Res. 2019;47(D1):D711-5.

Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 2014;513(7517):202-9.

Zheng H, Zhang G, Zhang L, Wang Q, Li H, Han Y, et al. Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis. Front Oncol. 2020;10:68.

Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19(1A):A68-77.

Sartor ITS, Zeidán-Chuliá F, Albanus RD, Dalmolin RJS, Moreira JCF. Computational analyses reveal a prognostic impact of TULP3 as a transcriptional expert regulator in pancreatic ductal adenocarcinoma. Mol Biosyst. 2014;10(6):1461-8.

Xue D, Cheng P, Han M, Liu X, Xue L, Ye C, et al. An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer. Onco Targets Ther. 2018;11:4755-68.

Poste G. Bring on the biomarkers. Nature. 2011;469:156-7.

Cheng L, Yang S, Yang Y, Zhang W, Xiao H, Gao H, et al. Global gene expression and functional network analysis of gastric cancer identify extended pathway maps and GPRC5A as a potential biomarker. Cancer Lett 2012;326(1):105-13.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.

Li Q, Birkbak NJ, Gyorffy B, Szallasi Z, Eklund AC. Jetset: selecting the optimal microarray probe set to represent a gene. BMC Bioinform. 2011;12:474.

Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 2011;12:35.

Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284-7.

Demšar J, Curk T, Erjavec A, Gorup Č, Hočevar T, Milutinovič M, et al. Orange: data mining toolbox in python. J Mach Learn Res. 2013;14(35):2349-53.

Fotiadis D, Kanai Y, Palacín M. The SLC3 and SLC7 families of amino acid transporters. Mol Aspects Med. 2013;34(2-3):139-58.

Asada K, Kobayashi K, Joutard S, Tubaki M, Takahashi S, Takasawa K, et al. Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer. Biomolecules. 2020;10(4):524.

El Ansari R, Alfarsi L, Craze ML, Masisi BK, Ellis IO, Rakha EA, et al. The solute carrier SLC7A8 is a marker of favourable prognosis in ER-positive low proliferative invasive breast cancer. Breast Cancer Res Treat. 2020;181(1):1-12.

Tina E, Prosén S, Lennholm S, Gasparyan G, Lindberg M, Göthlin Eremo A. Expression profile of the amino acid transporters SLC7A5, SLC7A7, SLC7A8 and the enzyme TDO2 in basal cell carcinoma. Br J Dermatol. 2019;180(1):130-40.

Barollo S, Bertazza L, Watutantrige-Fernando S, Censi S, Cavedon E, Galuppini F, et al. Overexpression of L-Type Amino Acid Transporter 1 (LAT1) and 2 (LAT2): Novel Markers of Neuroendocrine Tumors. PLoS One. 2016;11(5):e0156044.

Lv Y, Song Y, Ni C, Wang S, Chen Z, Shi X, et al. Overexpression of Lymphocyte Antigen 6 Complex, Locus E in Gastric Cancer Promotes Cancer Cell Growth and Metastasis. Cell Physiol Biochem. 2018;45(3):1219-29.

Upadhyay G. Emerging Role of Lymphocyte Antigen-6 Family of Genes in Cancer and Immune Cells. Front Immunol. 2019;10:819.

Nguyen TA, Bieging-Rolett KT, Putoczki TL, Wicks IP, Attardi LD, Pang KC. SIDT2 RNA Transporter Promotes Lung and Gastrointestinal Tumor Development. iScience. 2019;20:14-24.

Aizawa S, Contu VR, Fujiwara Y, Hase K, Kikuchi H, Kabuta C, et al. Lysosomal membrane protein SIDT2 mediates the direct uptake of DNA by lysosomes. Autophagy. 2017;13(1):218-22.

Beck A, Fecher-Trost C, Wolske K, Philipp SE, Flockerzi V, Wissenbach U. Identification of Sidt2 as a lysosomal cation-conducting protein. FEBS Lett. 2017;591(1):76-87.

Brady CA, Jiang D, Mello SS, Johnson TM, Jarvis LA, Kozak MM, et al. Distinct p53 transcriptional programs dictate acute DNA-damage responses and tumor suppression. Cell 2011;145(4):571-83.