Integrated Analysis Identifies an Immune‐Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer

Peng, Dongzhu; Gu, Bin; Ruan, Li-Ming; Zhang, Xingguo; Shu, Ping

doi:10.1155/2020/9780981

Cited by 4 publications

(5 citation statements)

References 43 publications

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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%

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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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Section: Resultsmentioning

confidence: 99%

“…In this strategy, authors established gene signatures from a specific gene group from typical cellular pathway. Peng et al identified an Immune-Based Prognostic Signature (Peng et al, 2020 ). Bai et al constructed six-gene signature based on DNA methylation-driven genes (Bai et al, 2020 ).…”

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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“…For predictive ability, the AUC was required to be equal to or higher than 0.65 with a 95% confidence interval (95% CI) excluding 0.5; an AUC ≥ 0.7 was considered to reflect good prediction or discrimination. We also compared the predictive ability of our gene signature with previously published prognostic signatures [17][18][19][20][21][22][23][24][25]. The Venkatraman permutation test was used to compare the paired ROC curves based on different signatures [26].…”

Section: Discussionmentioning

confidence: 99%

A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma

Liu

Wang

et al. 2022

BMC Gastroenterol

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Background Stomach adenocarcinoma (STAD) is a highly heterogeneous disease and is among the leading causes of cancer-related death worldwide. At present, TNM stage remains the most effective prognostic factor for STAD. Exploring the changes in gene expression levels associated with TNM stage development may help oncologists to better understand the commonalities in the progression of STAD and may provide a new way of identifying early-stage STAD so that optimal treatment approaches can be provided. Methods The RNA profile retrieving strategy was utilized and RNA expression profiling was performed using two large STAD microarray databases (GSE62254, n = 300; GSE15459, n = 192) from the Gene Expression Omnibus (GEO) and the RNA-seq database within the Cancer Genome Atlas (TCGA, n = 375). All sample expression information was obtained from STAD tissues after radical resection. After excluding data with insufficient staging information and lymph node number, samples were grouped into earlier-stage and later-stage. Samples in GSE62254 were randomly divided into a training group (n = 172) and a validation group (n = 86). Differentially expressed genes (DEGs) were selected based on the expression of mRNAs in the training group and the TCGA group (n = 156), and hub genes were further screened by least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the hub genes in distinguishing STAD stage in the validation group and the GSE15459 dataset. Univariate and multivariate Cox regressions were performed sequentially. Results 22 DEGs were commonly upregulated (n = 19) or downregulated (n = 3) in the training and TCGA datasets. Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression. The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively. Gene Set Enrichment Analysis (GSEA) was used to identify the potential stage-development pathways, including the PI3K-Akt and Calcium signaling pathways. Univariate Cox regression indicated that the nine-gene score was a significant risk factor for overall survival (HR = 1.28, 95% CI 1.08–1.50, P = 0.003). In the multivariate Cox regression, only SCRG1 was an independent prognostic predictor of overall survival after backward stepwise elimination (HR = 1.21, 95% CI 1.11–1.32, P < 0.001). Conclusion Through a series of bioinformatics and validation processes, a nine-gene signature that can distinguish STAD stage was identified. This gene signature has potential clinical application and may provide a novel approach to understanding the progression of STAD.

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“…26 In another study on GC, M2 macrophages were also significantly enriched in high-risk patients, as determined by the sixgene immune-based prognostic signature. 27 To study the immune-regulating role of consensus molecular subtype four in colorectal cancer (CRC), six immune genes (PROK1, THBS1, FGF11, CRP, S100A14, and CCL19) have been identified. 28 The immune prognostic signature allows the classification of patients into high-and low-risk groups in terms of relapse-free survival (RFS) and OS.…”

Section: Macrophage-related Immune Signaturesmentioning

confidence: 99%

“… 26 In another study on GC, M2 macrophages were also significantly enriched in high-risk patients, as determined by the six-gene immune-based prognostic signature. 27 …”

Section: Introductionmentioning

confidence: 99%

Immune gene signatures as prognostic criteria for cancer patients

Larionova

Таширева

2023

Ther Adv Med Oncol

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Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

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Integrated Analysis Identifies an Immune‐Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer

Cited by 4 publications

References 43 publications

Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma

Immune gene signatures as prognostic criteria for cancer patients

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