Identification of pathology-specific regulators of m6A RNA modification to optimize lung cancer management in the context of predictive, preventive, and personalized medicine.

RELEVANCE: Lung cancer is the most common malignant tumor with high morbidity (11.6% of the total diagnosed cancer cases) and mortality (18.4% of the total cancer deaths), and its 5-year survival rate is very low (20%). Clarification of any molecular events and the discovery of effective biomarkers will offer increasing promise for lung canner management. N6-methyladenosine (m6A) modification is one of the important RNA modifications that are closely associated with lung cancer, and are tightly regulated by m6A regulators. Elucidation of pathology-specific m6A regulators will directly contribute to lung cancer medical services in the context of predictive, preventive, and personalized medicine (PPPM).
PURPOSE: To investigate pathology-specific regulators of m6A RNA modifications in lung cancer and further inspect the m6A regulator gene signature as useful tools for PPPM in lung cancers.
METHODS: The gene expression data of 19 m6A regulators (m6A-methyltransferases-ZC3H13, KIAA1429, RBM15/15B, WTAP, and METTL3/14; demethylases-FTO and ALKBH5; and m6A-binding proteins-HNRNPC, YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, and HNRNPA2B1) and clinical data of 1013 lung cancer patients [511 lung adenocarcinoma (LUAD) and 502 lung squamous carcinoma (LUSC)] and 109 controls (Con) were obtained from the TCGA database. Quantitative real-time PCR (qRT-PCR) was used to verify m6A regulators in lung cancer cell lines. Protein-protein interaction (PPI), gene co-expression, survival analysis, and heatmap were used to analyze these m6A regulators in this set of lung cancer clinical data. Lasso regression was used to optimize the pathology-specific m6A regulator gene signature. Gene set enrichment analysis (GSEA) was used to reveal the functional characteristics of m6A regulators.
RESULTS: Those 19 m6A regulator profiling was significantly differentially expressed in lung cancer tissues relative to control tissues, which was also verified in lung cancer cell lines. Those m6A regulators interacted mutually, and those regulator-based sample clusters were correlated with clinical traits, including survival status, gender, tobacco smoking history, primary disease, and pathologic stage. Further, lasso regression based on the 19 m6A regulators optimized and identified a three-m6A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as independent prognostic factor, which classified 1013 lung cancer patients into high-risk and low-risk groups according to median value (0.84) of the lasso regression risk scores. This three-m6A-regulator signature profiling was significantly related to lung cancer overall survival, cancer status, and the above-described clinical traits. Further, GSEA revealed that KIAA1429, METTL3, and IGF2BP1 were significantly related to multiple biological behaviors, including proliferation, apoptosis, metastasis, energy metabolism, drug resistance, and recurrence, and that KIAA1429 and IGF2BP1 had potential target genes, including E2F3, WTAP, CCND1, CDK4, EGR2, YBX1, and TLX, which were associated with cancers.
CONCLUSION: This study provided the first view of the pathology-specific regulators of m6A RNA modification in lung cancers and identified the three-m6A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as an independent prognostic model to classify lung cancers into high- and low-risk groups for patient stratification, prognostic assessment, and personalized treatment toward PPPM in lung cancers. © European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2020.