Screening of Methylation Gene Sites as Prognostic Signature in Lung Adenocarcinoma

Abstract

Purpose: Most lung adenocarcinoma (LUAD) patients are diagnosed at the advanced stage and have poor prognosis. DNA methylation plays an important role in the prognosis prediction of cancers. The objective of this study was to identify new DNA methylation sites as biomarkers for LUAD prognosis.

Materials and methods: We downloaded DNA methylation data from The Cancer Genome Atlas data portal. Cox proportional hazard regression model and random survival forest algorithm were applied to identify the DNA-methylation sites. Methylation of sites were validated in the Gene Expression Omnibus cohorts. Function annotation were done to explore the biological function of DNA methylated sites signature.

Results: Six DNA methylation sites were identified as prognosis signature. The signature yielded acceptable discrimination between the high-risk group and low-risk group. The discrimination effect of this DNA methylation signature for the OS was obvious, with a median OS of 21.89 months vs. 17.74 months for high-risk vs. low-risk groups. This prognostic prediction model was validated by the test group and GEO dataset. The predictive survival value was higher for the prognostic prediction model than that for the tumor node metastasis stage. Adjuvant hemotherapy could not affect the prediction of the signature. Functional analysis indicated that these signature genes were involved in protein binding and cytoplasm.

Conclusion: We identified the prognostic signature for LUAD by combining six DNA methylation sites. This could service as potential robust and specificity signature in the prognosis prediction of LUAD.

Keywords: Lung adenocarcinoma; TCGA; methylation; prognosis signature.

Fig. 1. Flow chart of the study showing the analysis to develop the risk score model in training group and validate the efficiency of the model in test group. TCGA, The Cancer Genome Atlas; KM, Kaplan-Meier; ROC, receiver operating characteristic; GEO, Gene Expression Omnibus.

Fig. 2. Screening optimal model of all methylation gene sites. Univariable (A) and multivariable Cox (B) regression analysis of the association between the methylated sites signature and the survival of lung adenocarcinoma patients in training group. ^*p<0.05, ^†p<0.01, ^‡p<0.001; all the three symbols means there is a significant correlation.

Fig. 3. Methylated sites signature predicts prognosis of LUAD patients. Kaplan-Meier survival curves classified LUAD patients into high-risk and low-risk groups using the sites signature in the training and test datasets. p-values were calculated by log-rank test (A and B). Results of receiver operating characteristic analysis in the training and test datasets (C and D). LUAD, lung adenocarcinoma; AUC, area under curve.

Fig. 4. Validated the predictive power of the markers through the Kaplan-Meier survival analysis (A) and receiver operating characteristic analysis (B) in the individual dataset GSE56044. AUC, area under curve.

Fig. 5. Adjuvant chemotherapy could not divided the patients into high-risk group and the low-risk group (A). DNA methylation biomarker could separate the patients into high-risk group or low-risk group either the patients were used adjuvant hemotherapy nor none adjuvant chemotherapy (B). Adjuvant chemotherapy could not divided the patients into high-risk group and the low-risk group (C and D).

Fig. 6. Multivariable Cox (A) regression analysis and nomogram of combined methylated sites signature and clinical variables predict patients' overall survival (B). ^*p<0.05, there is a significant correlation.

Fig. 7. Validated methylation sites (cg01883425, cg08536228, cg10511249, cg15875437, cg18465286, cg25320115) of signature in independent GEO cohorts (A-F). It showed the results were the same as the ones we identified from TCGA data, which was all the gene sites in the tumor samples were hypermethylated. ^*p<0.05, ^†p<0.01; means there is a significant difference between the normal and tumor. GEO, Gene Expression Omnibus; TCGA, The Cancer Genome Atlas.