Open Access

Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets

  • Authors:
    • Mingyuan Luan
    • Fucheng Song
    • Shuyuan Qu
    • Xi Meng
    • Junjie Ji
    • Yunbo Duan
    • Changgang Sun
    • Hongzong Si
    • Honglin Zhai
  • View Affiliations

  • Published online on: July 29, 2020     https://doi.org/10.3892/ol.2020.11919
  • Article Number: 58
  • Copyright: © Luan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Lung cancer is a major cause of cancer‑associated mortality worldwide. However, the association between multi‑omics data and survival in lung cancer is not fully understood. The present study investigated the performance of the methylation survival risk model in multi‑platform integrative molecular subtypes and aimed to identify copy number (CN) variations and mutations that are associated with survival risk. The present study analyzed 439 lung adenocarcinoma cases based on DNA methylation, RNA, microRNA (miRNA), DNA copy number and mutations from The Cancer Genome Atlas datasets. First, six cancer subtypes were identified using integrating DNA methylation, RNA, miRNA and DNA copy number data. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to extract methylation sites of survival model and calculate the methylation‑based survival risk indices for all patients. Survival for patients in the high‑risk group was significantly lower compared with that for patients in the low‑risk group (P<0.05). The present study also assessed methylation‑based survival risks of the six subtypes and analyzed the association between survival risk and non‑silent mutation rate, number of segments, fraction of segments altered, aneuploidy score, number of segments with loss of heterozygosity (LOH), fraction of segments with LOH and homologous repair deficiency. Finally, the specific copy number regions and mutant genes associated with the different subtypes were identified (P<0.01). Chromosome regions 17q24.3 and 11p15.5 were identified as those with the most survival risk‑associated copy number variation regions, while a total of 29 mutant genes were significantly associated with survival (P<0.01).
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October-2020
Volume 20 Issue 4

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Spandidos Publications style
Luan M, Song F, Qu S, Meng X, Ji J, Duan Y, Sun C, Si H and Zhai H: Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets. Oncol Lett 20: 58, 2020
APA
Luan, M., Song, F., Qu, S., Meng, X., Ji, J., Duan, Y. ... Zhai, H. (2020). Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets. Oncology Letters, 20, 58. https://doi.org/10.3892/ol.2020.11919
MLA
Luan, M., Song, F., Qu, S., Meng, X., Ji, J., Duan, Y., Sun, C., Si, H., Zhai, H."Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets". Oncology Letters 20.4 (2020): 58.
Chicago
Luan, M., Song, F., Qu, S., Meng, X., Ji, J., Duan, Y., Sun, C., Si, H., Zhai, H."Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets". Oncology Letters 20, no. 4 (2020): 58. https://doi.org/10.3892/ol.2020.11919