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Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver
Shiqi Li1, Xin Chen1, Jianfang Chen1
1College of Chemistry, Sichuan University, Chengdu, 610064, China.
Abstract:
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 methylations, 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.
Insights
This study integrates multi-omics data to identify cancer-related G protein-coupled receptors (GPCRs). Combining multi-staged and deep learning approaches reveals 165 common GPCRs, highlighting their immune-related roles in cancer.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- G protein-coupled receptors (GPCRs) represent the largest drug target family, yet their role in cancer therapy remains underexplored due to limited understanding of their cancer associations.
- Investigating GPCRs in cancer using multi-omics data is crucial, but integrating complex datasets presents significant challenges.
Purpose of the Study:
- To comprehensively characterize GPCRs in 33 cancer types by integrating multi-omics data, including somatic mutations, copy number alterations, DNA methylation, and mRNA expression.
- To identify novel cancer-related GPCRs by employing and comparing two distinct data integration strategies: multi-staged and meta-dimensional approaches.
- To explore the immune-related functions of GPCRs in the context of cancer.
Main Methods:
- Employed a multi-staged integration strategy to analyze correlations between GPCR mutations, somatic copy number alterations (SCNAs), DNA methylation, and mRNA expression.
- Utilized deep learning models for a meta-dimensional integration analysis to predict potential oncogenic GPCRs.
- Compared and contrasted findings from both integration strategies to identify common and unique cancer-related GPCRs.
Main Results:
- GPCR mutations poorly predicted expression dysregulation; SCNA-expression correlations were mostly positive, while methylation-expression and methylation-SCNA correlations were predominantly negative.
- Identified 32 GPCRs linked to aberrant SCNAs and 144 GPCRs linked to aberrant methylation.
- Deep learning predicted over 100 GPCRs as potential oncogenes. A total of 165 GPCRs were consistently identified by both integration strategies, while 172 were unique to one strategy.
- GPCRs, particularly class A and adhesion receptors, exhibit significant immune-related associations.
Conclusions:
- The integration of multi-omics data is essential for a comprehensive understanding of GPCRs in cancer.
- Both multi-staged and meta-dimensional integration strategies are valuable and complementary for identifying cancer-related GPCRs.
- The identified GPCRs, especially the 165 common ones, represent promising candidates for future cancer therapy research and warrant further investigation into their immune-related functions.
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