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

Abstract

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.

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Abbreviations

ALKBH5 :

AlkB homolog 5, RNA demethylase

CCND1 :

cyclin D1 [Homo sapiens]

CDK4 :

cyclin-dependent kinase 4

DNA :

deoxyribonucleic acid

E2F3 :

E2F transcription factor 3

EBV :

Epstein–Barr virus

EGFR-TKI :

epidermal growth factor receptor tyrosine kinase inhibitors

EGR2 :

early growth response 2

EMT :

epithelial–mesenchymal transition

FDR :

false discovery rate

FTO :

FTO alpha-ketoglutarate dependent dioxygenase

GSEA :

gene set enrichment analysis

HBV :

hepatitis virus B

HNRNPA2B1 :

heterogeneous nuclear ribonucleoprotein A2/B1

HNRNPC :

heterogeneous nuclear ribonucleoprotein C

HPV :

human papilloma virus

HR :

hazard ratio

HTLV-1 :

human T cell leukemia/lymphoma virus I

IGF2BP1 :

insulin-like growth factor 2 mRNA-binding protein 1

IGF2BP2 :

insulin-like growth factor 2 mRNA-binding protein 2

IGF2BP3 :

insulin-like growth factor 2 mRNA-binding protein 3

IL6 :

interleukin 6

KIAA1429 :

Vir-like m6A methyltransferase associated

lncRNAs :

long noncoding RNAs

LUAD :

lung adenocarcinoma, LUSC lung squamous carcinoma

m 6 A :

N6-methyladenosine

METTL14 :

methyltransferase-like 14

METTL3 :

mRNA mA methyltransferase

mRNAs :

messenger RNAs

ncRNAs :

noncoding RNAs

PAC :

proportion of ambiguous clustering

PD-1 :

programmed cell death 1

PD-L1 :

CD274 molecule

PPI :

protein–protein interaction

qRT-PCR :

quantitative real-time polymerase chain reaction

RBM15 :

RNA-binding motif protein 15

RBM15B :

RNA-binding motif protein 15B

RNA :

ribonucleic acid

rRNAs :

ribosomal RNAs

snRNA :

small nuclear RNA

TCGA :

The Cancer Genome Atlas

TLX :

nuclear receptor subfamily 2 group E member 1

tRNAs :

transfer RNAs

USP7 :

ubiquitin-specific protease 7

WTAP :

WT1-associated protein

YBX1 :

Y-box binding protein 1

YTHDC1 :

YTH domain containing 1

YTHDC2 :

YTH domain containing 2

YTHDF1 :

YTH N6-methyladenosine RNA-binding protein 1

YTHDF2 :

YTH N6-methyladenosine RNA-binding protein 2

YTHDF3 :

YTH N6-methyladenosine RNA-binding protein 3

ZC3H13 :

zinc finger CCCH-type containing 13

Abbreviations for all particular genes and proteins can be found at the following link::

https://www.ncbi.nlm.nih.gov/gene/

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Acknowledgments

We would like to thank The Cancer Genome Atlas (TCGA) project organizers as well as all study participants for providing the publicly available TCGA RNA-seq data and clinical data.

Funding

The authors acknowledge the financial supports from the Shandong First Medical University Talent Introduction Funds (to X.Z.), the Hunan Provincial Hundred Talent Plan (to X.Z.), and the Central South University Graduate Student Exploration Innovative Project 2019 (Grant No. 206021701).

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Contributions

N.L. performed bioinformatic analysis, carried out the experiments, prepared the figures and tables, and designed and wrote the manuscript. X.Z. conceived the concept, instructed experiments, supervised results, coordinated, critically revised/wrote manuscript, and was responsible for its financial supports and the corresponding works. All authors approved the final manuscript.

Corresponding author

Correspondence to Xianquan Zhan.

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The authors declare that they have no competing interests.

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All the patients were informed about the purposes of the study and, consequently, have signed their “consent of the patient” form. All investigations conformed to the principles outlined in the Declaration of Helsinki and were performed with permission by the responsible Medical Ethics Committee of Xiangya Hospital, Central South University, China.

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Li, N., Zhan, X. Identification of pathology-specific regulators of m6A RNA modification to optimize lung cancer management in the context of predictive, preventive, and personalized medicine. EPMA Journal (2020). https://doi.org/10.1007/s13167-020-00220-3

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Keywords

  • Lung cancer
  • m6A regulators
  • Gene signature
  • Prognostic model
  • KIAA1429
  • METTL3
  • IGF2BP1
  • Biomarker
  • Clinical traits
  • Predictive preventive personalized medicine (PPPM)
  • Molecular patterns
  • Patient stratification