The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Wang, Guoguang; Zhan, Tian; Li, Fan; Shen, Jian; Gao, Xiang; Xu, Lei; Li, Yuan; Zhang, Jianping

doi:10.7150/jca.49658

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…Based on TNM stage characteristics, 22 overlapping DEGs were identified between the TCGA set and the training set. This number is similar to that in a prognosis-based study [37], but is far less than those obtained in other data mining studies that have focused on gene expression between tumor and normal tissue. This suggests that either the homogeneity or heterogeneity among gastric adenocarcinomas is much more complex.…”

Section: Discussionsupporting

confidence: 73%

“…This confirms the robust performance of the model. This model also avoided overfitting and the Simpson's Paradox, which are risks when performing bioinformatics analysis and model building [37][38][39][40]. More importantly, our signature had higher accuracy for stage prediction than previous signatures focusing on various prognostic features.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 73%

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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“… 6 However, the vast majority of studies have concentrated mainly on a single gene, and its predictive ability is insufficient compared with multiple biomarker-based models. 7 In clinical practice, the more accurately a patient’s OS stage can be predicted, the sooner the clinician can make the clinical decision. Lipid decomposition and anabolism of tumor cells, which are different from normal cells, have attracted more and more attention, and it is also a new hotspot of tumor targeted therapy.…”

Section: Introductionmentioning

confidence: 99%

Identification of a Lipid Metabolism-Associated Gene Signature Predicting Survival in Breast Cancer

Gong¹,

Liu²,

Wu³

et al. 2021

IJGM

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Background Cancer metabolism and specifically lipid metabolism play an important role in breast cancer (BC) progression and metastasis. However, the role of lipid metabolism-associated genes (LMGs) in the prognosis of breast cancer remains unknown. Methods The expression profiles and clinical follow-up information of 1053 BC were downloaded from The Cancer Genome Atlas (TCGA), and metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expressed metabolism-related genes. Then, the formula of the metabolism-related risk model was composed, and the risk score of each patient was calculated. The breast cancer patients were divided into high-risk and low-risk groups with a cutoff of the median expression value of the risk score, and the prognostic analysis was also used to analyze the survival time between these two groups. Finally, we analyzed the expression, interaction and correlation among the lipid metabolism-associated genes risk model. Results The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group in TCGA, and single-sample gene set enrichment analysis (ssGSEA) shows it is plausible that lipid metabolism is highly correlated with tumor immunity. Conclusion Lipid metabolism-associated genes may become a new prognostic indicator predicting the survival of BC patients. The prognostic genes (n = 16) may help provide new strategies for tumor therapy.

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PAX5/ITGAX Contributed to the Progression of Atherosclerosis by Regulation of B Differentiation via TNF-α Signaling Pathway

Wang

Guan

Tang

et al. 2023

DNA and Cell Biology

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The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Cited by 3 publications

References 40 publications

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

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

Identification of a Lipid Metabolism-Associated Gene Signature Predicting Survival in Breast Cancer

PAX5/ITGAX Contributed to the Progression of Atherosclerosis by Regulation of B Differentiation via TNF-α Signaling Pathway

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