An Innovative Prognostic Model Based on Four Genes in Asian Patient with Gastric Cancer

Chen, Jiahui; Wang, Anqiang; Ji, Jun Ho; Zhou, Kai; Bu, Zhaode; Lyu, Guoqing; Ji, Jiafu

doi:10.4143/crt.2020.424

Cited by 14 publications

(11 citation statements)

References 29 publications

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“…Previous researchers have identified new prognostic biomarkers. The prognostic model established by 4 genes (RBPMS2, RGN, PLEKHS, and CT83) was a reliable tool to predict the OS of GC patients in Asia (32), and this was also consistent with our bioinformatics analysis and experimental results. There are some limitations to our study.…”

Section: Discussionsupporting

confidence: 86%

Identification of lncRNAs based on different patterns of immune infiltration in gastric cancer

Chen¹,

Ben²,

Guo³

et al. 2022

J Gastrointest Oncol

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Background: Gastric cancer is one of the most common malignant tumors in the world, which brings great challenges to people's life and health. The purpose of this study was to investigate immune related-lncRNAs and identify new biomarkers for the prognosis of gastric cancer (GC).Methods: We downloaded data from The Cancer Genome Atlas (TCGA) and used R software to determine the ESTIMATEScore, ImmuneScore, and StromalScore of each tumor sample. We performed prognostic analysis and identified the differentially expressed lnRNAs, which were then used to construct a prognostic model. Among the 44 hub genes in the competitive endogenous RNA (ceRNA) network, 3 differentially expressed genes were verified by qPCR.Results: Based on the degree of immune infiltration, cluster A had a higher ESTIMATEScore, ImmuneScore, and StromalScore and higher expression levels of PDL1 (CD274) and CTLA4 than cluster B. Univariate Cox analysis was conducted for these differential lncRNAs, and 57 lncRNAs were found to have prognostic value (P<0.05). gene cluster A had a worse prognosis than gene cluster B (P=0.021). Then, a prognostic model was constructed. The low-risk group had a significantly higher survival rate. Finally, the qPCR results showed that the expression levels of BMPER, PRUNE2, and RBPMS2 were low in GC cell lines. Conclusions:We identified a risk score of 19 lncRNAs as a prognostic marker of GC. There was a relationship between these 19 prognostic-related lncRNAs and the subtypes of infiltrating immune cells. An approach for predicting the prognosis of GC was therefore provided in this study.

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Section: Discussionsupporting

confidence: 86%

Identification of lncRNAs based on different patterns of immune infiltration in gastric cancer

Chen¹,

Ben²,

Guo³

et al. 2022

J Gastrointest Oncol

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“…These findings coincided with Chen et al [ 18 ], who reported PLEKHS1 as one of the four genes of the prognostic-protective marker for predicting the overall survival (OS) of GC using univariate, multivariate, and Lasso Cox regression analyses but Zhang et al [ 21 ] showed PLEKHS1 upregulation in the intestinal-type-GC and it was considered as one of a five-gene signature for the disease using The Cancer Genome Atlas (TCGA) database.…”

Section: Discussionsupporting

confidence: 79%

“…The current study aimed to evaluate PLEKHS1, AADAC and CDKN3 gene expressions in GC because they were included in gene-signature for GC in some bioinformatics analysis studies [ 13 , 18 – 20 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of gene expression of PLEKHS1, AADAC, and CDKN3 as novel genomic markers in gastric carcinoma

et al. 2022

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Gastric cancer (GC) is considered lethal aggressive cancer. In Egypt, GC has a low incidence but unfortunately, it is mostly diagnosed at an advanced stage with a poor prognosis. Assessment of novel markers that can be used in the early detection of GC is an urgent need. The present study was performed to assess the association of the Pleckstrin homology domain-containing S1 (PLEKHS1)، arylacetamide deacetylase (AADAC, and Cyclin-dependent kinase inhibitor 3 (CDKN3) genes with GC and to correlate their gene expression levels with tumor stage, grade, and other clinicopathological features. The current work was performed on forty gastric tissue samples; twenty in Group 1 with GC tissues at different stages, and grades and twenty in Group 2 (control group) with non-tumorous tissue. PLEKHS1, AADAC, and CDKN3 gene expression were assessed by RT-qPCR. AADAC, CDKN3 genes were significantly (p<0.001) upregulated, while PLEKHS1 gene was significantly (p<0.001) downregulated in the GC group than the control group. AADAC gene expression exhibited a high significant (p<0.001) positive correlation with the tumor grades and the tumor stages. A high significant negative correlation between AADAC, and CDKN3 gene expression (r = -.760, p<0.001) was found. The three studied parameters showed high significant sensitivity and specificity in the prediction of the presence of GC. PLEKHS1, AADAC, and CDKN3 gene expressions were suggested to be used as diagnostic and predictive biomarkers of GC, additionally, AADAC may be used as a prognostic marker in these patients for further future confirming studies.

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“…Additionally, we tested the predictive power of the 9-gene signature alone, and the results showed that it was a great predictor for OS (AUC = 0.765 in the training set; AUC = 0.703 in the validation set). Compared to a 4-gene signature reported by a previous study, our signature acquired a higher level (AUC for OS, 0.765 vs 0.684) [ 19 ]. Furthermore, we integrated the risk score with clinical factors (age, sex, pathological stage) to construct a multivariate Cox regression model.…”

Section: Discussionmentioning

confidence: 58%

Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population

Chu

Sun

et al. 2022

PLoS ONE

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Objective Gastric cancer (GC) is one of the most common tumour diseases worldwide and has poor survival, especially in the Asian population. Exploration based on biomarkers would be efficient for better diagnosis, prediction, and targeted therapy. Methods Expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Survival-related genes were identified by gene set enrichment analysis (GSEA) and univariate Cox. Then, we applied a Bayesian hierarchical lasso Cox model for prognostic signature screening. Protein-protein interaction and Spearman analysis were performed. Kaplan–Meier and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prediction performance. Multivariate Cox regression was used to identify prognostic factors, and a prognostic nomogram was constructed for clinical application. Results With the Bayesian lasso Cox model, a 9-gene signature included TNFRSF11A, NMNAT1, EIF5A, NOTCH3, TOR2A, E2F8, PSMA5, TPMT, and KIF11 was established to predict overall survival in GC. Protein-protein interaction analysis indicated that E2F8 was likely related to KIF11. Kaplan-Meier analysis showed a significant difference between the high-risk and low-risk groups (P<0.001). Multivariate analysis demonstrated that the 9-gene signature was an independent predictor (HR = 2.609, 95% CI 2.017–3.370), and the C-index of the integrative model reached 0.75. Function enrichment analysis for different risk groups revealed the most significant enrichment pathway/term, including pyrimidine metabolism and respiratory electron transport chain. Conclusion Our findings suggested that a novel prognostic model based on a 9-gene signature was developed to predict GC patients in high-risk and improve prediction performance. We hope our model could provide a reference for risk classification and clinical decision-making.

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An Innovative Prognostic Model Based on Four Genes in Asian Patient with Gastric Cancer

Cited by 14 publications

References 29 publications

Identification of lncRNAs based on different patterns of immune infiltration in gastric cancer

Identification of lncRNAs based on different patterns of immune infiltration in gastric cancer

Evaluation of gene expression of PLEKHS1, AADAC, and CDKN3 as novel genomic markers in gastric carcinoma

Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population

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