Serum biomarker panels for the diagnosis of gastric cancer

Wu, Dan; Zhang, Pinglu; Ma, Jianmin; Xu, Jibin; Li, Yang; Xu, Wei; Que, Hai-Feng; Chen, Mei-Fen; Xu, Hongtao

doi:10.1002/cam4.2055

Cited by 99 publications

(82 citation statements)

References 35 publications

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“…GHR was recently reported to mediate cell progression and apoptosis via the BRAF/MEK/ERK signaling pathway in breast cancer (26). According to the literature, GHR was elevated in GC serum samples and high expression of GHR mRNA was associated with a poor outcome in GC patients (27,28), which suggested that GHR may serve as novel biomarkers for the early diagnosis and prognosis determination of GC.…”

Section: Discussionmentioning

confidence: 99%

A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

Chen

Liu

et al. 2020

Front. Oncol.

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PurposeThe exploration and interpretation of DNA methylation-driven genes might contribute to molecular classification, prognostic prediction and therapeutic choice. In this study, we built a prognostic risk model via integrating analysis of the transcriptome and methylation profile for patients with gastric cancer (GC).MethodsThe mRNA expression profiles, DNA methylation profiles and corresponding clinicopathological information of 415 GC patients were downloaded from The Cancer Genome Atlas (TCGA). Differential expression and correlation analysis were performed to identify DNA methylation-driven genes. The candidate genes were selected by univariate Cox regression analyses followed by the least absolute shrinkage and selection operator (LASSO) regression. A prognostic risk nomogram model was then built together with clinicopathological parameters.Results5 DNA methylation-driven genes (CXCL3, F5, GNAI1, GAMT and GHR) were identified by integrated analyses and selected to construct the prognostic risk model with clinicopathological parameters. High expression and low DNA hypermethylation of F5, GNAI1, GAMT and GHR, as well as low expression and high DNA hypomethylation of CXCL3 were significantly associated with poor prognosis rates, respectively. The high-risk group showed a significantly shorter prognosis than the low-risk group in the TCGA dataset (HR = 0.212, 95% CI = 0.139–0.322, P = 2e-15). The final nomogram model showed high predictive efficiency and consistency in the training and validation group.ConclusionWe construct and validate a prognostic nomogram model for GC based on five DNA methylation-driven genes with high performance and stability. This nomogram model might be a powerful tool for prognosis evaluation in the clinic and also provided novel insights into the epigenetics in GC.

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Section: Discussionmentioning

confidence: 99%

A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

Chen

Liu

et al. 2020

Front. Oncol.

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“…Great efforts have been made to the diagnose and treat this disease; however, the mortality rate has remained essentially unchanged worldwide in the past decades (3). Currently, clinicians extensively use diagnostic and prognostic biomarkers to improve the clinical course of GC (4,5). However, gastric carcinoma involves mixed cell types with variable degrees of differentiation, and the majority of serum biomarkers have low sensitivity and low positive predictive value, such as CEA, CA19-9 and CA72-4 (6)(7)(8).…”

Section: Introductionmentioning

confidence: 99%

Serum Metabolic Profiling Analysis of Chronic Gastritis and Gastric Cancer by Untargeted Metabolomics

et al. 2021

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PurposeGastric cancer is a common tumor of the digestive system. Identification of potential molecules associated with gastric cancer progression and validation of potential biomarkers for gastric cancer diagnosis are very important. Thus, the aim of our study was to determine the serum metabolic characteristics of the serum of patients with chronic gastritis (CG) or gastric cancer (GC) and validate candidate biomarkers for disease diagnosis.Experimental DesignA total of 123 human serum samples from patients with CG or GC were collected for untargeted metabolomic analysis via UHPLC-Q-TOF/MS to determine characteristics of the serum. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and heat map were used for multivariate analysis. In addition, commercial databases were used to identify the pathways of metabolites. Differential metabolites were identified based on a heat map with a t-test threshold (p < 0.05), fold-change threshold (FC > 1.5 or FC < 2/3) and variable importance in the projection (VIP >1). Then, differential metabolites were analyzed by receiver operating characteristic (ROC) curve to determine candidate biomarkers. All samples were analyzed for fasting lipid profiles.ResultsAnalysis of serum metabolomic profiles indicated that most of the altered metabolic pathways in the three groups were associated with lipid metabolism (p < 0.05) and lipids and lipid-like molecules were the predominating metabolites within the top 100 differential metabolites (p < 0.05, FC > 1.5 or FC < 2/3, and VIP >1). Moreover, differential metabolites, including hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine had high diagnostic performance according to PLS-DA. In addition, fasting lipid profile analysis showed the serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (Apo-A1) were decreased concomitant to the progression of the progression of the disease compared with those in the control group (p < 0.05).ConclusionsThus, this study demonstrated that lipid metabolism may influence the development of CG to GC. Hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine were selected as candidate diagnostic markers for CG and GC.

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“…The poor prognosis could be because most people were diagnosed with advanced disease. Identification of new biomarkers and potential mechanisms are urgent to improve prognosis of DGC [4].…”

Section: Introductionmentioning

confidence: 99%

Identification of the co-expression module-trait relationships and hub genes associated with the diffuse-type gastric cancer

Ye¹,

Zheng²,

Zeng³

et al. 2021

Preprint

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Diffuse-type gastric cancer (DGC) is a gastric cancer subtype based on Laurén histological type, with poor prognosis when compared intestinal-type gastric cancer. It is necessary that comprehensive analysis identifies pathways and genes involved in DGC. RNA expression data of DGC (including 52 samples, top 5000 genes) and fourteen patient clinic traits were downloaded from The Cancer Genome Atlas (TCGA) database. Co-expression modules and module-trait relationships were constructed by weighted gene co-expression network analysis (WGCNA). The identified ten co-expression modules were obtained from DGC samples. The co-expression turquoise module positively correlated with longest dimension. The co-expression magenta module positively correlates with gender, and person neoplasm cancer status. The co-expression green module also positively correlates with pathologic N stage and pathologic stage. Besides, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment obtained some cancer related signal pathways in the co-expression turquoise and green module, such as proliferation, drug resistance, cell metabolism. Additionally, we identified the hub genes in the co-expression turquoise, green and magenta modules. Protein-protein interaction (PPI) and biological processes (BP) analysis were performed on the hub genes in those modules, suggesting the key role of those modules in the development of the cancer. The hub genes were significantly related to Overall survival analysis showed some important hub genes were significant in DGC patients, including ANK2, GNAO1, MAP6, RERG (turquoise module), MEP1A, GUCY2C, ALDOB (green module), XIST, TTTY15, NCRNA00185, CYorf15B (magenta module). The study provided new biomarkers and potential mechanisms in DGC.

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Serum biomarker panels for the diagnosis of gastric cancer

Cited by 99 publications

References 35 publications

A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

Serum Metabolic Profiling Analysis of Chronic Gastritis and Gastric Cancer by Untargeted Metabolomics

Identification of the co-expression module-trait relationships and hub genes associated with the diffuse-type gastric cancer

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