College of Chemistry, Sichuan University, Chengdu, 610064, China.
Comput Biol Med. 2023 Jul;161:106988. doi: 10.1016/j.compbiomed.2023.106988. Epub 2023 May 11.
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.
G 蛋白偶联受体（GPCRs）是最大的药物靶标家族。不幸的是，由于对它们与癌症的相关性知之甚少，GPCR 在癌症治疗中的应用非常有限。多组学数据使 GPCR 的系统研究成为可能，但由于数据的复杂性，它们的有效整合仍然是一个挑战。在这里，我们采用两种整合策略，多阶段和元维度方法，来充分描述 33 种癌症中 GPCR 的体细胞突变、体细胞拷贝数改变（SCNAs）、DNA 甲基化和 mRNA 表达。多阶段整合的结果表明，GPCR 突变不能很好地预测表达失调。表达与 SCNAs 之间的相关性主要是正相关，而甲基化与表达和 SCNAs 的相关性呈双峰分布，负相关占主导地位。基于这些相关性，分别鉴定出由异常 SCNAs 和甲基化驱动的 32 个和 144 个潜在的与癌症相关的 GPCR。此外，还通过使用深度学习模型进行了元维度整合分析，预测了 100 多个可能的致癌 GPCR。在比较两种整合策略的结果时，有 165 个与癌症相关的 GPCR 在两种策略中都很常见，这表明它们应该在未来的研究中优先考虑。然而，有 172 个 GPCR 只出现在一种策略中，这表明两种整合策略应该同时考虑，以补充另一种策略遗漏的信息，从而获得更全面的理解。最后，相关性分析进一步表明，GPCR 特别是 A 类和黏附受体，通常与免疫有关。总的来说，这项工作首次揭示了不同组学层之间的关联，并强调了在识别与癌症相关的 GPCR 时结合这两种策略的必要性。
