A 5-Gene Prognostic Combination for Predicting Survival of Patients with Gastric Cancer

Song, Lina; Wang, Xiaoyan; He, Xiaofeng

doi:10.12659/msm.914815

Cited by 6 publications

(4 citation statements)

References 37 publications

(49 reference statements)

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“…Furthermore, comparison of the RiskScores between molecular subtypes showed that the RiskScores of the C1 subtype with a poorer prognosis were significantly higher than those of the C2 subtype with a better prognosis, which is consistent with the previous findings of this study. Compared with three previously reported prognostic models for GC (20)(21)(22), the model established in this study incorporated fewer genes, was more operational in clinical practice and had the highest C-index value, indicating that its overall performance was better than that of the other three models. To the best of our knowledge, this study is the first to construct a prognostic model using tumour invasion-related genes, which can provide more insights into the role of prognostic models in the However, this study has several limitations.…”

Section: Discussionmentioning

confidence: 72%

Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features

et al. 2022

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BackgroundThis study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.MethodologyTumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster samples via non-negative matrix factorisation (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway and GO functional enrichment analyses were conducted using the WebGestaltR package (v0.4.2). The immune scores of molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter and the ssGSEA function of the GSVA package. Univariate, multivariate and lasso regression analyses of DEGs were performed using the coxph function of the survival package and the glmnet package to construct a RiskScore model. The robustness of the model was validated using internal and external datasets, and a nomogram was constructed based on the model.ResultsBased on 97 tumour invasion-related genes, 353 GC samples from TCGA were categorised into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs were identified between the two subtypes; of which, four genes were selected to construct the risk model. This four-gene signature was robust and exhibited stable predictive performance in different platform datasets (GSE26942 and GSE66229), indicating that the established model performed better than other existing models.ConclusionA prognostic stratification system based on a four-gene signature was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this system as a molecular diagnostic test is recommended to assess the prognostic risk of patients with GC.

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Section: Discussionmentioning

confidence: 72%

Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features

et al. 2022

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“…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 ).…”

Section: Resultsmentioning

confidence: 99%

Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

Xie

Cai

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Gastric cancer (GC) is the second leading cause of cancer mortality and remains the fourth common cancer worldwide. The effective and feasible methods for predicting the possible outcomes for GC patients are still lacking. While genetic profiling might be suitable in some way, the application of gene expression signatures has been show to be a robust tool. Here, by performing a comprehensive search in PubMed, we provided an up-to-date summary of 39 prognostic gene signatures for GC patients, and described the processing procedure of the selection, calculation and construction of gene signature. We also reviewed current web tools including PROGgene and SurvExpress that can be used to analyze the prognostic value of multiple genes for GC. This review will aid in comprehensive understanding of the current prognostic gene signatures to accurately predict the outcome of GC patients, and may guide the future clinical management when the reliability of these signatures is validated in clinics.

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“…Bao et al (2019) established a 3-gene signature of diffuse type GC for explaining the molecular mechanism of poor prognosis. In another study, a 5-gene signature was established that can be an independent prognostic factor in GC patients (Song et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization

Cui

Wang

Ning

et al. 2021

Front. Cell Dev. Biol.

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Gastric Cancer (GC) is a common cancer worldwide with a high morbidity and mortality rate in Asia. Many prognostic signatures from genes and non-coding RNA (ncRNA) levels have been identified by high-throughput expression profiling for GC. To date, there have been no reports on integrated optimization analysis based on the GC global lncRNA-miRNA-mRNA network and the prognostic mechanism has not been studied. In the present work, a Gastric Cancer specific lncRNA-miRNA-mRNA regulatory network (GCsLMM) was constructed based on the ceRNA hypothesis by combining miRNA-target interactions and data on the expression of GC. To mine for novel prognostic signatures associated with GC, we performed topological analysis, a random walk with restart algorithm, in the GCsLMM from three levels, miRNA-, mRNA-, and lncRNA-levels. We further obtained candidate prognostic signatures by calculating the integrated score and analyzed the robustness of these signatures by combination strategy. The biological roles of key candidate signatures were also explored. Finally, we targeted the PHF10 gene and analyzed the expression patterns of PHF10 in independent datasets. The findings of this study will improve our understanding of the competing endogenous RNA (ceRNA) regulatory mechanisms and further facilitate the discovery of novel prognostic biomarkers for GC clinical guidelines.

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A 5-Gene Prognostic Combination for Predicting Survival of Patients with Gastric Cancer

Cited by 6 publications

References 37 publications

Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features

Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features

Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization

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