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A Trans-omics Assessment of Gene-gene Interaction in Early-stage NSCLC

Abstract

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with P  ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (β  = 0.018, P = 1.87 × 10 ), which mapped to RELA × HLA-G (β  = 0.218, P = 8.82 × 10 ) and cg08872738 × cg27077312 (β  = -0.010, P = 1.16 × 10 ), which mapped to TUBA1B × TOMM40 (β =-0.250, P = 3.83 × 10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

Citing Articles

Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.

Sun N, Wang Y, Chu J, Han Q, Shen Y Cancer Genomics Proteomics. 2023; 20(6suppl):669-678.

PMID: 38035701 PMC: 10687732. DOI: 10.21873/cgp.20414.

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