Open Access

A curated target gene pool assisting disease prediction and patient‑specific biomarker selection for lung squamous cell carcinoma

  • Authors:
    • Bin Huang
    • Ning Zhong
    • Hongbao Cao
    • Guiping Yu
  • View Affiliations

  • Published online on: July 31, 2018     https://doi.org/10.3892/ol.2018.9241
  • Pages: 5140-5146
  • Copyright: © Huang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

There have been hundreds of genes demonstrated to be associated with lung squamous cell carcinoma (LSCC), presenting various degrees of association with this disease. In the present study, gene vectors were investigated as genetic biomarkers for the diagnosis and personalized treatment of LSCC. A LSCC genetic database (LSCC_GD) was developed through literature‑associated data analysis, where 260 LSCC target genes were curated. Subsequently, numerous associations between these genes and LSCC were studied. Following this, a sparse representation‑based variable selection (SRVS) was employed for gene selection from two LSCC gene expression datasets, followed by a case/control classification. Results were compared using analysis of variance (ANOVA)‑based gene selection approaches. Using SRVS, a gene vector was selected from each dataset, resulting in significantly higher classification accuracy (CR), compared with randomly selected genes (For datasets GSE18842 and GSE1987, CR=100 and 100% and permutation P=5.0x10‑4 and 1.8x10‑3, respectively). The SRVS method outperformed ANOVA in terms of the classification ratio. The results indicated that, for a given dataset, there may be a gene vector from the 260 curated LSCC genes that possesses significant prediction power. SRVS is effective in identifying the optimum gene subset target for personalized treatment.
View Figures
View References

Related Articles

Journal Cover

October-2018
Volume 16 Issue 4

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

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Huang B, Zhong N, Cao H and Yu G: A curated target gene pool assisting disease prediction and patient‑specific biomarker selection for lung squamous cell carcinoma. Oncol Lett 16: 5140-5146, 2018
APA
Huang, B., Zhong, N., Cao, H., & Yu, G. (2018). A curated target gene pool assisting disease prediction and patient‑specific biomarker selection for lung squamous cell carcinoma. Oncology Letters, 16, 5140-5146. https://doi.org/10.3892/ol.2018.9241
MLA
Huang, B., Zhong, N., Cao, H., Yu, G."A curated target gene pool assisting disease prediction and patient‑specific biomarker selection for lung squamous cell carcinoma". Oncology Letters 16.4 (2018): 5140-5146.
Chicago
Huang, B., Zhong, N., Cao, H., Yu, G."A curated target gene pool assisting disease prediction and patient‑specific biomarker selection for lung squamous cell carcinoma". Oncology Letters 16, no. 4 (2018): 5140-5146. https://doi.org/10.3892/ol.2018.9241