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Multicenter Study
. 2018 Jun;12(6):913-924.
doi: 10.1002/1878-0261.12204. Epub 2018 May 4.

A multi-omic study reveals BTG2 as a reliable prognostic marker for early-stage non-small cell lung cancer

Sipeng Shen  1   2   3 , Ruyang Zhang  1   3 , Yichen Guo  2 , Elizabeth Loehrer  2 , Yongyue Wei  1   3 , Ying Zhu  1   3 , Qianyu Yuan  2 , Sebastian Moran  4 , Thomas Fleischer  5 , Maria M Bjaanaes  5 , Anna Karlsson  6 , Maria Planck  6 , Johan Staaf  6 , Åslaug Helland  5   7 , Manel Esteller  4 , Li Su  2   3 , Feng Chen  1   3   8 , David C Christiani  2   3   9
Affiliations
Multicenter Study

A multi-omic study reveals BTG2 as a reliable prognostic marker for early-stage non-small cell lung cancer

Sipeng Shen et al. Mol Oncol. 2018 Jun.

Abstract

B-cell translocation gene 2 (BTG2) is a tumour suppressor protein known to be downregulated in several types of cancer. In this study, we investigated a potential role for BTG2 in early-stage non-small cell lung cancer (NSCLC) survival. We analysed BTG2 methylation data from 1230 early-stage NSCLC patients from five international cohorts, as well as gene expression data from 3038 lung cancer cases from multiple cohorts. Three CpG probes (cg01798157, cg06373167, cg23371584) that detected BTG2 hypermethylation in tumour tissues were associated with lower overall survival. The prognostic model based on methylation could distinguish patient survival in the four cohorts [hazard ratio (HR) range, 1.51-2.21] and the independent validation set (HR = 1.85). In the expression analysis, BTG2 expression was positively correlated with survival in each cohort (HR range, 0.28-0.68), which we confirmed with meta-analysis (HR = 0.61, 95% CI 0.54-0.68). The three CpG probes were all negatively correlated with BTG2 expression. Importantly, an integrative model of BTG2 methylation, expression and clinical information showed better predictive ability in the training set and validation set. In conclusion, the methylation and integrated prognostic signatures based on BTG2 are stable and reliable biomarkers for early-stage NSCLC. They may have new applications for appropriate clinical adjuvant trials and personalized treatments in the future.

Keywords: BTG2; early-stage non-small cell lung cancer; multi-omic; prognosis.

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Figures

Figure 1
Figure 1
Flow chart indicating study design. The whole study could be divided into three parts. First, we used the methylation data to compare the difference between tumour and normal tissue, build a prognostic model, and validate it in the different cohorts. Secondly, we used the gene expression data to evaluate the BTG2 expression and overall survival by meta‐analysis. Lastly, we performed an integration analysis based on clinical information, methylation and expression data.
Figure 2
Figure 2
Methylation analysis for BTG2. (A) HR with 95% confidence interval (95% CI) of the 13 CpG sites in Cox regression analysis in the training set. The top three probes were significantly associated with survival. (B) Methylation differential comparison of the three probes between tumour and adjacent normal tissues. Data were described as mean and SD. **FDR‐q < 0.001; *FDR‐q < 0.05. (C–H) Kaplan–Meier survival analyses of the methylation prognostic model, which were categorized into low‐risk and high‐risk groups using a cut‐off value of the median value in the training set for (C) Harvard, (D) Sweden, (E) Spain, (F) Norway, (G) GDC, and (H) overall dataset.
Figure 3
Figure 3
Gene expression analysis for BTG2. (A) BTG2 expression differential analysis between tumour and adjacent normal tissues. Data were described as mean and SD. (B) Meta‐analysis with fixed‐effect model for the BTG2 expression and early‐stage lung cancer survival collected from our cohorts and 17 extended public datasets. (C) Kaplan–Meier survival analyses for the cases in the meta‐analysis. Patients were categorized into low‐risk and high‐risk groups using a cut‐off value of the median value within each cohort.
Figure 4
Figure 4
Integration analysis for BTG2. (A) Correlation analysis for the three CpG probes and BTG2 expression using a linear regression model. Expression values within each cohort were dichotomized by the median value and combined. Methylation beta‐values were described as mean and SD. **FDR‐q < 0.001. (B) Kaplan–Meier survival analyses of the integrated prognostic model, which were categorized into low‐risk and high‐risk groups using a cut‐off value of the median value in the training set and validation set. (C) C‐index with standard error bar are shown in the two sets, including clinical information (C), gene expression (E) and methylation (M). The integration model (C + E + M) showed the best predictive performance.
Figure 5
Figure 5
Stratification analysis for the methylation and integration prognostic signatures. (A) HR with 95% CI of overall survival for the overall cases in different subgroups stratified by clinical parameters for the integration model. (B) Kaplan–Meier curves for the cases with adjuvant therapy. (C) Kaplan–Meier curves regarding overall survival for respective different score categories in integration model.
Figure 6
Figure 6
Flowchart for BTG2‐involved pathways and biological mechanisms.
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