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

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.

Exploring TCGA database for identification of potential prognostic genes in stomach adenocarcinoma

“…Extracellular region ANGPTL1 [27], BCHE, C7, CARTPT [28], CPXM2 [29], PRG4, VIP Extracellular matrix OMD [30], MGP [31], KERA, SFRP2 [32], SPARCL1 [33] Cytoplasm PDLIM3 [34], MYL9 [35], FHL1 [36], DES, SYNC, CPED1…”

Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

“…Elaborate search results of prognostic signatures in GC were shown in Table 1 and Figure 2 (Chen et al, 2005 ; Motoori et al, 2005 ; Xu et al, 2009 ; Takeno et al, 2010 ; Cho et al, 2011 ; Bauer et al, 2012 ; Kim et al, 2012 ; Wang et al, 2013 , 2017b , 2018 ; Lee et al, 2014 ; Pasini et al, 2014 ; Li et al, 2016 ; Zhao et al, 2016 , 2019 ; Hou et al, 2017 ; Kuang et al, 2017 ; Lafrenie et al, 2017 ; Liu et al, 2018 , 2019 ; Peng et al, 2018 , 2020 ; Smyth et al, 2018 ; Wu et al, 2018 ; Yuzhalin et al, 2018 ; Chang and Lai, 2019 ; Chang et al, 2019 ; Dai et al, 2019 ; Jiang et al, 2019 , 2020 ; Song et al, 2019 ; Bai et al, 2020 ; Guan et al, 2020 ). Briefly, we got 39 literatures in NCBI PubMed Database following the above procedure ( Figure 1 ).…”

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

“… 43 DNA methylation profiling analysis and validation experiments showed that NAV1 was significantly hypomethylated in breast cancers; 44 while overexpression of members of the neuron navigator gene family was reported to promote invasion and predict poor prognosis. 45 , 46 The breast tumours that overexpressed CBX2 had a clear reduction in DNA methylation. 47 Elevated CBX2 expression was correlated with poor clinical outcome in breast cancer 47 and PCa cohorts.…”