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A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer

  • Authors:
    • Hui Jiang
    • Jiming Gu
    • Jun Du
    • Xiaowei Qi
    • Chengjia Qian
    • Bojian Fei

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  • Published online on: November 21, 2019     https://doi.org/10.3892/mmr.2019.10841
  • Pages: 347-359

  • Copyright: © Jiang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Gastric cancer (GC) ranks fifth in terms of incidence and third in terms of tumor mortality worldwide. The present study was designed to construct a Support Vector Machine (SVM) classifier and risk score system for GC. The GSE62254 (training set) and GSE26253 (validation set 2) datasets were downloaded from the Gene Expression Omnibus database. Furthermore, the gene expression profile of GC (validation set 1) was obtained from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) between recurrent and non‑recurrent samples were determined using the limma package. The feature genes were selected using the Caret package, and an SVM classifier was built using the e1071 package. Using the penalized package, the optimal predictive genes for constructing a risk score system were screened. Finally, stratification analysis of clinical factors and pathway enrichment analysis were performed using Gene Set Enrichment Analysis. A total of 239 DEGs were identified in GSE62254, among which 114 DEGs were significantly associated with both recurrence‑free survival and overall survival. Subsequently, 21 feature genes were screened from the 114 DEGs, and an SVM classifier was built. A risk score system for survival prediction was constructed, following the selection of 10 optimal genes, including A‑kinase anchoring protein 12, angiopoietin‑like protein 1, cysteine‑rich sequence 1, myeloid/lymphoid or mixed‑lineage leukemia, translocated to chromosome 11, neuron navigator 3, neurobeachin, nephroblastoma overexpressed, pleiotrophin, tumor suppressor candidate 3 and zinc finger and SCAN domain containing 18. The stratification analysis revealed that pathological stage was an independent prognostic clinical factor in the high‑risk group. Additionally, eight significant pathways were associated with the 10‑gene signature. The SVM classifier and risk score system may be applied for classifying and predicting the prognosis of patients with GC, respectively.
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January-2020
Volume 21 Issue 1

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Spandidos Publications style
Jiang H, Gu J, Du J, Qi X, Qian C and Fei B: A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer. Mol Med Rep 21: 347-359, 2020
APA
Jiang, H., Gu, J., Du, J., Qi, X., Qian, C., & Fei, B. (2020). A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer. Molecular Medicine Reports, 21, 347-359. https://doi.org/10.3892/mmr.2019.10841
MLA
Jiang, H., Gu, J., Du, J., Qi, X., Qian, C., Fei, B."A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer". Molecular Medicine Reports 21.1 (2020): 347-359.
Chicago
Jiang, H., Gu, J., Du, J., Qi, X., Qian, C., Fei, B."A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer". Molecular Medicine Reports 21, no. 1 (2020): 347-359. https://doi.org/10.3892/mmr.2019.10841