Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients.

Autor:	Velásquez Sotomayor MB; Immunology and Cancer Research Group (IMMUCA), Lima, Peru.; Escuela de Medicina Humana, Facultad de Ciencias de la Salud, Universidad Científica del Sur, Lima, Perú., Campos Segura AV; Immunology and Cancer Research Group (IMMUCA), Lima, Peru.; Biochemistry and Molecular Biology Research Laboratory, Faculty of Natural Sciences and Mathematics, Universidad Nacional Federico Villarreal, Lima, Peru.; Laboratory of Genomics and Molecular Biology, International Center of Research CIPE, A.C. Camargo Cancer Center, Sao Paulo, Brazil., Asurza Montalva RJ; Immunology and Cancer Research Group (IMMUCA), Lima, Peru.; Escuela de Medicina Humana, Facultad de Ciencias de la Salud, Universidad Científica del Sur, Lima, Perú., Marín-Sánchez O; Immunology and Cancer Research Group (IMMUCA), Lima, Peru.; Departamento Académico de Microbiología Médica, Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Lima, Peru., Murillo Carrasco AG; Immunology and Cancer Research Group (IMMUCA), Lima, Peru.; Centro de Investigação Translacional em Oncologia (LIM24), Departamento de Radiologia e Oncologia, Faculdade de Medicina da Universidade de São Paulo and Instituto do Câncer do Estado de São Paulo, São Paulo, Brazil., Ortiz Rojas CA; Immunology and Cancer Research Group (IMMUCA), Lima, Peru.; Laboratório de Investigação Médica (LIM) 31, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.
Jazyk:	angličtina
Zdroj:	Frontiers in genetics [Front Genet] 2023 Sep 12; Vol. 14, pp. 1206609. Date of Electronic Publication: 2023 Sep 12 (Print Publication: 2023).
DOI:	10.3389/fgene.2023.1206609
Abstrakt:	Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression ( p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91 , DYNC1I1 , FAM83D , LBH , SLITRK5 , WTIP , and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Velásquez Sotomayor, Campos Segura, Asurza Montalva, Marín-Sánchez, Murillo Carrasco and Ortiz Rojas.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu