Identification and prognostic value of metabolism-related genes in gastric cancer

Fang, Wen; Huang, Jiani; Lu, Xiaona; Huang, Wenjie; Wang, Yulan; Bai, Yingfeng; Ruan, Shuai; Gu, Suping; Chen, Xiaoxue; Shu, Ping

doi:10.18632/aging.103838

Cited by 38 publications

(36 citation statements)

References 37 publications

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“…It was significantly decreased in gastric tumor samples (Kropotova et al, 2013) and cervical squamous cell carcinoma samples (Peng et al, 2015). RDH12 was also one of the differentially expressed metabolism-related genes, and correlated with the prognosis of gastric cancer patients (Wen et al, 2020).…”

Section: Discussionmentioning

confidence: 93%

“…INHBA, Inhibin-βA (INHBA), a ligand belonging to the transforming growth factorβ superfamily (Oshima et al, 2014), is associated with cell proliferation in various tumor types including colon adenocarcinoma (Lin et al, 2020a;Miao et al, 2020;Miyamoto et al, 2020), pancreatic cancer (Liu et al, 2020), gastric cancer as well as oral squamous cell carcinoma (Lin et al, 2020b). Many studies have demonstrated the prognostic role of INHBA in colon adenocarcinoma (Chen et al, 2020;Li et al, 2020; Miao Sun et al, 2020), and the role of INHBA in gastric cancer has been widely reported also. INHBA was highly expressed (Kaneda et al, 2011;Seeruttun et al, 2019;Zhang et al, 2010) and aberrantly methylated in gastric tumor samples, and high INHBA expression was associated with significantly poorer 5-year survival than low expression group (Katayama et al, 2017;Wang et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

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Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis

Sun

et al. 2021

PeerJ

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Background Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy. Methods GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the “least absolute shrinkage and selection operator” (LASSO) logistic regression. Finally, hub genes were validated in the GSE54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm. Results Twelve modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these five modules. Then, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CWH43, GRIK3, INHBA, RDH12, SCNN1G, SIGLEC11 and LYVE1 were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through artificial neural network algorithm with the area under the receiver operating characteristic curve at 0.946. Conclusions These hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis

Sun

et al. 2021

PeerJ

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“…It was significantly decreased in gastric tumor samples (Kropotova et al 2013) and cervical squamous cell carcinoma samples (Peng et al 2015). 2020:08:51875:2:1:NEW 19 Nov 2020) Manuscript to be reviewed RDH12 was also one of the differentially expressed metabolism-related genes, and correlated with the prognosis of gastric cancer patients (Wen et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis (v0.2)"

2021

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Background: Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy.Methods: GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the "least absolute shrinkage and selection operator" (LASSO) logistic regression. Finally, hub genes were validated in GS54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm.Results: 12 modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these four modules. Then,

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“…To build the prognostic model, univariate Cox regression analysis, LASSO regression, and multivariate Cox regression analysis were applied using R software (v3.6.1) based on the methodology of previously published studies [ 15 – 17 ]. Firstly, univariate analysis was performed to screen the significant DE immune genes based on the detected immune genes using the “survival” package.…”

Section: Methodsmentioning

confidence: 99%

Construction and Validation of a 7-Immune Gene Model for Prognostic Assessment of Esophageal Carcinoma

2020

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Background We constructed a predictive risk model of esophageal carcinoma (EC) for prognostic prediction. Material/Methods Immune genes and the expression data were downloaded from the ImmPort database and The Cancer Genome Atlas database. Univariate analysis, Lasso regression, and multivariate analysis were applied to screen the ultimately included prognostic immune genes for the model based on the training cohort. Survival analysis and receiver operating characteristic (ROC) curve were applied to evaluate the model. The model was further validated in the testing and entire cohorts, and the clinical utility of the model and its ability to assess the subtypes of EC were evaluated in the entire cohort. Results We detected 297 differentially expressed immune genes, including 241 upregulated genes and 56 downregulated genes in EC patients. Based on these genes, we developed a 7-immune gene model of EC, including HSPA6, S100A12, NOS2, DKK1, OSM, AR, and OXTR. The area under the curve (AUC) of the model at 1 year was 0.825. Similarly, the AUC values for the validating cohorts were 0.813 and 0.816, respectively. Pathological stage and risk score of the model were independent prognostic factors. This model was effective for both subtypes of EC. Conclusions We constructed a 7-gene model consisting of HSPA6, S100A12, NOS2, DKK1, OSM, AR, and OXTR. This risk model could be used for prognostic prediction of EC.

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Identification and prognostic value of metabolism-related genes in gastric cancer

Cited by 38 publications

References 37 publications

Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis

Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis

Peer Review #1 of "Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis (v0.2)"

Construction and Validation of a 7-Immune Gene Model for Prognostic Assessment of Esophageal Carcinoma

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