Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methyla-tions, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune -related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.

Automated summary

This study utilizes two integration strategies to comprehensively analyze GPCRs and reveals that mutations cannot effectively predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of methylation and SCNAs with expressions exhibit bimodal patterns. 32 potential cancer-related GPCRs driven by aberrant SCNA and 144 potential cancer-related GPCRs driven by aberrant methylation are identified. Deep learning models predict over 100 GPCRs as potential oncogenes. The study emphasizes the importance of combining both integration strategies to identify cancer-related GPCRs and reveals the immune-related nature of GPCRs.