Background: Gastric Cancer (GC) continues to be a leading cause of global cancer morbidity and mortality, representing the third-leading cause of cancer death worldwide. The absence of significant symptoms on early GC leads to diagnosis of GC at advanced stages, which is associated with a poor prognosis and a high mortality rate, being particularly elevated in Latin America, and stressing the need of finding specific biomarkers to trace GC progression in distinct populations. Genetic variation among populations and the interpatient and intratumor heterogeneity of the tumors are the main hitches to identifying molecular pathways driving the complex GC architecture, which, if solved, could substantially contribute to improve patient diagnosis and outcomes. With the increasing availability of NGS technologies and the improvement of bioinformatics and statistics for the analysis genome-wide data, integrative approaches providing a more comprehensive view of the hallmarks of cancer have arisen, such as it is the identification and analysis of expression quantitative trait loci (eQTLs) using transcriptomic and genotyping data. GC is a complex disease partially controlled by oncogenes and tumor suppressor genes that are further regulated by microRNAs (miRNAs) and other non- coding RNAs such as circular RNAs (circRNAs). Both types of RNAs are known to act in a coordinated way controlling several biological processes related to cancer, such as proliferation and apoptosis, through intricated circRNA–miRNA–mRNA regulatory networks. Most known circRNAs act as miRNA sponges by sequestering them and avoiding the repressive miRNA regulatory function over their target genes. It is the case of circHIPK3, which has dual roles in cancer progression by sponging different miRNAs such as miR-29b, and thus regulating the target mRNAs in complex regulatory networks yet not fully understood. Due to the significance of circHIPK3 in GC, there is a need to clarify the multiple effects and underlying mechanisms of action of this circRNA.
Hypothesis: Differential expression of circRNAs among tissues at different stages of GC progression combined with eQTL analysis of circulating circRNAs in Chilean population may lead to the identification of circRNAs with an active role in GC that may be functionally characterized.
Objectives: To identify and functionally characterize circRNAs that are involved in GC in a Chilean population as miRNA sponges and discover eQTLs that affect their expression and that may be used to trace GC progression. The specific objectives are: 1: To identify circRNAs associated with the progression of GC by tissue-based differential expression; 2: To identify cis-eQTLs regulating circRNAs involved in GC progression that may be used to predict circulating circRNA expression; 3: To validate preselected circRNAs associated with the progression of GC as miRNA sponges and 4: To functionally characterize potential circRNA-miRNA-mRNA axes involved in the progression of GC.
Methodology: We plan to identify candidate circRNAs for GC progression through tissue-based differential expression analysis by RNAseq on total RNA from gastric mucosa biopsies on three different diagnostic groups in the sequence “non-atrophic gastritis -> intestinal metaplasia -> gastric cancer” by RNA-seq using Oxford Nanopore technology, non-parametric and machine learning approaches will be used to identify candidate circRNAs with monotonically changes in the expression among the groups. In addition we plan to identify cis circRNA eQTLs by analysing the association between genome-wide genotypes and the expression of the preselected circRNA from in the serum of control volunteers from which genome-wide genotyping data is available. The most robust circRNAs candidates, according to level of expression (log2 expression) and the statistical significance (False Discovery Rate (FDR)), will be chosen for further functional analysis of candidate circRNA-miRNA pairs by circRNA and miRNA overexpression and/or silencing in tumoral and normal gastric cells, as well as in organoids and in a Drosophila model of cancer, and the analysis of some molecular and cellular phenotypes associated with the progression of cancer (e.g., proliferation, apoptosis, migration, metastasis).
Expected results: we expect to acquire a better understanding on the role of circRNAs sponging miRNAs in the regulation of processes related to the progression of GC and a database of eQTLs influencing the expression of specific circulating circRNAs for the studied Chilean population. Identifying genetic variability that may explain a substantial fraction of circulating circRNA expression might be useful to infer GC risk from SNV data and to identify candidate circRNAs as biomarkers.