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Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

3816 - A prediction panel with DNA methylation biomarkers for lung adenocarcinoma

Date

20 Oct 2018

Session

Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

Presenters

Nan Shen

Citation

Annals of Oncology (2018) 29 (suppl_8): viii14-viii57. 10.1093/annonc/mdy269

Authors

N. Shen1, Y. Pan2, X. Mo3

Author affiliations

  • 1 Department Of Rehabilitation, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, 200092 - Shanghai/CN
  • 2 Functional Genome Analysis, Deutsches Krebsforschungszentrum, 69120 - Heidelberg/DE
  • 3 Institute For Pediatric Translational Medicine, Shanghai Children’s Medical Center, 200000 - Shanghai/CN
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Resources

Abstract 3816

Background

Lung adenocarcinoma accounts for more than 40% of lung cancer incidence. Thus, it is urgent to identify early-stage related markers. In this study, the effectiveness of CpG methylation on predicting lung adenocarcinoma was investigated.

Methods

In total, 1,170 patients with lung adenocarcinoma from four independent databases and one medical center were sorted by three phases. In the discovery phase, 338 lung adenocarcinomas and nonmalignant samples were collected from the GEO databases which used Illumina Infinium HumanMethylation27K BeadChip for the methylation analysis. The K-Means Clustering algorithm was used to select significant CpGs. In the training phase, recursive feature elimination was performed to evaluate the importance of selected CpGs to classification model. In the validation phase, four candidate CpGs were validated using two cohorts (n = 832 and n = 10). To explore the potential biological function of selected CpGs, GO enrichment analysis was performed using the Database for DAVID version 6.8.

Results

After the selection of CpGs by the K-Means Clustering algorithm, 62 CpGs showed great different methylation profiles between lung adenocarcinomas and adjacent nonmalignant lung tissue (p < 0.05). Among these selected CpGs, 95.16% were hypermethylated in the malignant samples comparing to only 4.84% were hypomethylated. With the evaluation of recursive feature elimination, four CpGs corresponding to HOXA9, KRTAP8-1, CCND1, and TULP2 were highlighted as candidate predictors in the training phase. The performance of these four candidate CpGs were validated in two validation cohorts (p < 0.01). These disparate hypermethylated genes were significantly enriched in GO biological processes including negative regulation of transcription from RNA polymerase II promoter, DNA-templated transcription, while the hypomethylated gene was obviously enriched with the terms including adenylate cyclase-activating G-protein coupled receptor signaling pathway. The direction of methylation did not affect the enrichments for Out-CpG sites.

Conclusions

A four-CpG-based signature, including HOXA9, KRTAP8-1, CCND1 and TULP2, is useful for the prediction of lung adenocarcinoma.

Clinical trial identification

Legal entity responsible for the study

Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University.

Funding

Has not received any funding.

Editorial Acknowledgement

Disclosure

All authors have declared no conflicts of interest.

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