piRNA-eQTL aims to systematically evaluate the effects of germline genetic variants on PIWI-interacting RNA expression in 33 cancer types from The Cancer Genome Atlas (TCGA) database.
When the user selects a cancer type or enters a piRNA name or SNP ID, a table will be built to display the query results. Users can download the results of cis-piRNA eQTLs for each cancer type by clicking the 'Download' button. In addition, users can select one SNP-piRNA pair and click the 'Plot' button, and a vector diagram of the boxplot is provided to display the association between the SNP genotypes and piRNA expression. Note: The eQTL results with sample size < 100 should be interpreted with caution.
Besides, to control the type I error, we also suggest users used the following P-value (at FDR = 0.1) to identify eSNPs and epiRNAs:
When the user selects a cancer type or enters a piRNA name, a table will be built to display the query results. In addition, two boxplot diagrams are used to display the difference in the piRNA expression between independent and paired tumor and normal samples.
When the user selects a cancer type or enters a piRNA name or SNP ID, a table will be built to display the query results. For piRNA, users can also select a different threshold value (i.e., percentile) from the slider box to split patients into high- and low-expressed groups. Besides, two diagrams of KM plot are provided to display the associations of piRNA expression and SNP genotypes with overall survival probability.
When the user selects a cancer type or enters a piRNA name or SNP ID, a table will be built to display the query results. In addition, users can select a different LD threshold value from the slider box to explore more potential eSNPs associated with GWAS traits.
When the user enters a batch of piRNA names and/or SNP IDs, we provided 3 modules to display the query results, including 'Pan-cancer piRNA expression profile', 'Summary of pan-cancer eQTL analysis (Tumor)' and 'Summary of pan-cancer eQTL analysis (Normal)'.
Future directions:
a. Previous studies found that somatic mutations also played an important role in the development of cancer. In addition to mutations in protein-coding regions, these studies described the landscape of non-coding mutations in cancer, particularly in promoter and enhancer regions, and their role in regulating gene expression and protein functions. Therefore, it is noteworthy that somatic mutations (e.g., single-nucleotide variants [SNVs], small insertions and deletions, genomic rearrangements and structural variations) would also affect gene expression extending in piRNAs. A systematic analysis between somatic mutations and piRNA expression is needed to be further studied and incorporated into this database.
b. We will update the piRNA-eQTL database to provide more accurate cis-piRNA eQTL results with sufficient sample size by incorporating other databases (e.g., Gene Expression Omnibus [GEO] dataset).