Search

4056 Background: Epstein–Barr virus (EBV) discriminates a molecular group of gastric cancer (GC) that seems to have peculiar treatment responses and prognosis. Nevertheless, the gene expression pattern of such tumors needs additional investigations due to the heterogeneity of the tumor microbiome and the small number of studied samples. Biological networks analysis of gene expression patterns, in this complex scenario, might lead to the discovery of putative "hubs" genes, supposed to have important roles in cancer occurrence and consequences. This study aimed to explore the biological value of hub genes in EBV-positive gastric cancer patients. Methods: Fresh tumor samples collected during gastrectomies from individuals with gastric adenocarcinoma (n = 41, 59.0±11.0 years) were included. Gene expression of tumor samples was evaluated in the Illumina sequencing platform. A weighted correlation network analysis (WGCNA) was used to find highly correlated gene patterns. The gene's correlation to EBV status was also explored, including eight EBV positive and 33 EBV negative cases. Gene Ontology (GO) term enrichment was performed to predict the involved biological functions. The expression level of the hubs gene was measured by differential expression analysis. Results: A gene cluster including 636 genes able to discriminate EBV status (ρ = 0.65, p-value = 6e-05) was identified. GO analysis (padj 1 and padj< 0.05). The top hub genes were LAPTM5, PTPN22, C1QA, CD84, CD53, ADAMDEC1, DPYD, TYROBP, RARRES3, IFI16, CYBB, CMKLR1, PHF21A, GPNMB, and C1QC, according to cluster significance and gene expression level. In positive EBV-GC, all hub genes were overexpressed (FC 1.24 - 2.49) (Table). The average area under the ROC curve (AUC) for hub genes was 0.9 (0.84-0.99). Overexpression of LAPTM5 (p-value = 0.013) and DPYD (p-value =0.0065) and low expression of ADAMDEC1 (p-value = 0.05) were strongly related to poor survival outcome. Conclusions: EBV-positive status was correlated with overexpression of hub genes. Genes found in this investigation have high sensitivity and specificity to discriminate EBV status in GC. Some of them seem to be related to patients' survival, opening an avenue for future scientific explorations in this field. The biological network's approach may be a promising tool for mining target genes for potential clinical applications. [Table: see text]

e16056 Background: FLOT perioperative have emerged as a new standard for locally advanced gastric cancer (LAGC). Nevertheless, the molecular consequences of such therapy remain obscure. Aiming to investigate changes in the tumor transcriptional landscape in LAGC patients treated with FLOT, a comprehensive analysis of messenger RNA (mRNAs) and long noncoding RNA (lncRNAs) expression was developed using next-generation sequencing (NGS). Methods: Paired tumor and non-tumor fresh samples, including 17 patients with intestinal-type LAGC submitted to neoadjuvant FLOT treatment (treated group) and 13 who did not receive neoadjuvant therapy (untreated group), were sequenced and compared. Differential expression analyses were performed using the DESeq2 package. Criteria for differentially expressed (DE) lncRNAs and mRNAs were|Log2(Fold-Change)| > 2; and adjusted p-value < 0.05. In silico analyses were adopted to identify DE lncRNAs with transcription factors (TFs) and RNA-binding proteins (RPBs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and gene ontology (GO) was also performed to elucidate these target proteins’ and mRNAs biological’s roles. Results: In tumor samples from the untreated group, 4443 mRNAs and 961 lncRNAs DE were found, in which 293 mRNAs and 148 lncRNAs accurately discriminated (the area under the curve ROC > 0.9) tumor and non-tumor tissues. Only 265 mRNAs and 29 lncRNAs DE were detected in treated patients, indicating that neoadjuvant therapy led to changes in expression patterns, making tumor tissues similar to non-tumor tissues. Subsequent analyzes identified that non-tumor tissues (treated vs. untreated) showed similar expression profiles. Among tumor samples, 34 mRNAs and 54 lncRNAs DE were found, in which 7 mRNAs and 16 lncRNAs can discriminate between treated and untreated tumors, indicating the effect of neoadjuvant treatment is predominant in the tumor. GO evaluation of genes was significant for 83 terms for biological process. The most annotated terms were the regulation of catalytic activity. We also observed that DE lncRNAs interact with TFs and RBPs were enriched in related cancer development and progression pathways, including transcriptional dysregulation and cellular senescence. Conclusions: Despite the need for further validation to better understand the transcriptional landscape related to neoadjuvant treatment, the present study highlights the functional relevance of lncRNAs in tumor biology and, mainly, shed light on the molecular effects of FLOT neoadjuvant treatment, that might help to guide future investigations aiming to establish personalized management of GC.

80 Background: Recently, some papers aiming to warn the scientific community regarding possible misinterpretation due to using adjacent to tumor sample tissues as normal controls were published The main motivation for these alerts was the observation of important molecular alterations in tissues nearby a cancer, although been collected from non-tumor areas. Nevertheless the cancer field hypothesis has been recognized many years ago; this concept was not incorporated to current practice in molecular investigations. In this work we aim to investigate potential implications of using adjacent to tumor sample tissues as normal controls. Methods: Gastric Cancer (GC) and Adjacent Tissues (AT) paired fresh samples of 16 patients, and 16 samples from non-cancer patients (NC) were analyzed. miRNA sequencing was performed using Illumina Miseq platform. Statistical analysis was performed in R using DESeq2 tool to identify differential expressed miRNAs. Results: Comparing GC with NC tissue samples, we observed 21 miRNAs differentially expressed (p-value < 0.05 and |Log2(Fold-Change)| > 2), eight were down-regulated and 13 were up-regulated in GC. Comparing AT with NC tissue samples, 16 miRNAs were differentially expressed, two were down-regulated and 14 were up-regulated in AT. In both comparisons, GC and AT samples clustered together and clearly separated from NC samples. Comparing GC with AT samples, hsa-miR-196a-5p was significantly down-regulated in GC. Conclusions: AT tissues harbor molecular alterations distinguishing them from non-cancer patient’s samples. Combined analyses of the groups clearly demonstrated that AT resembles much more cancer profile than NC patient’s tissue prolife. Using AT samples as controls might impair the discovery of possible initial molecular events identifiable exclusively by using samples from non-cancer patients. The current strategy of molecular investigation must be revised.