Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

Overview

Journal Chest

Publisher Elsevier

Specialty Pulmonary Medicine

Date 2020 Mar 2

PMID 32113923

Citations 22

Authors

Ruyang Zhang

Chao Chen

Xuesi Dong

Sipeng Shen

Linjing Lai

Jieyu He

Dongfang You

Lijuan Lin

Ying Zhu

Hui Huang

Jiajin Chen

Liangmin Wei

Xin Chen

Yi Li

Yichen Guo

Weiwei Duan

Liya Liu

Li Su

Andrea Shafer

Thomas Fleischer

Maria Moksnes Bjaanaes

Anna Karlsson

Maria Planck

Rui Wang

Johan Staaf

Aslaug Helland

Manel Esteller

Yongyue Wei

Feng Chen

David C Christiani

Affiliations

Soon will be listed here.

Abstract

Background: DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions.

Research Question: Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis?

Study Design And Methods: Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.

Results: Twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC, 0.88 [95% CI, 0.83-0.93]; and AUC, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively.

Interpretation: The integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.

Citing Articles

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Verma S, Magazzu G, Eftekhari N, Lou T, Gilhespy A, Occhipinti A Cell Rep Methods. 2024; 4(7):100817.

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Tan H, Zeng L, Yang S, Hou L, Wu J, Cai X Sci Rep. 2024; 14(1):14639.

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Biomarkers and experimental models for cancer immunology investigation.

Xu H, Jia Z, Liu F, Li J, Huang Y, Jiang Y MedComm (2020). 2023; 4(6):e437.

PMID: 38045830 PMC: 10693314. DOI: 10.1002/mco2.437.

Construction and validation of cuproptosis-related lncRNA prediction signature for bladder cancer and immune infiltration analysis.

Li H, Jiang H, Huang Z, Chen Z, Chen N Aging (Albany NY). 2023; 15(16):8325-8344.

PMID: 37616061 PMC: 10496989. DOI: 10.18632/aging.204972.

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