scholarly journals Immunogenomic characterization in gastric cancer identifies microenvironmental and immunotherapeutically relevant gene signatures

2021 ◽  
Author(s):  
Xiao Han ◽  
Heyue Lu ◽  
Xiaojun Tang ◽  
Yao Zhao ◽  
Hongxue Liu
Keyword(s):  
Gastric Cancer ◽  
Gene Signatures ◽  
Relevant Gene

Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures

2019 ◽  
Vol 7 (5) ◽  
pp. 737-750 ◽  
Author(s):  
Dongqiang Zeng ◽  
Meiyi Li ◽  
Rui Zhou ◽  
Jingwen Zhang ◽  
Huiying Sun ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Tumor Microenvironment ◽  
Gene Signatures ◽  
Relevant Gene

Identification of 10 Gene Signatures as Prognostic Biomarkers for Peritoneal Metastatic Gastric Cancer

2021 ◽  
Author(s):  
Junliang Li ◽  
Lingfang Zhang ◽  
Tiankang Guo
Keyword(s):  
Gastric Cancer ◽  
Clustering Algorithm ◽  
Transforming Growth Factor ◽  
Cox Regression ◽  
Risk Groups ◽  
Metastatic Gastric Cancer ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Gene Signatures

Abstract Background. Peritoneal metastatic gastric cancer (PMGC) is very common, and usually, the prognosis is poor. There is currently an absence of accurate methods for the early diagnosis and prediction of peritoneal metastasis (PM). This highlights the need to develop strategies to identify the risk of PMGC. Methods. We performed a comprehensive discovery of biomarkers to predict PM by analyzing profiling datasets from GSE62254. The prognostic PM-related genes were obtained using the univariate Cox regression analysis, followed by a least absolute shrinkage and selection operator regression (LASSO) to establish a risk score model. The gene set enrichment analysis (GSEA) was used to determine the pathway enrichment in both the high- and low-risk groups. The 1-, 3-, and 5-year overall survival (OS) rates and area under the receiver operating characteristic curve (ROC) were used to compare the predictive accuracy-based risk stratification. In addition, an unsupervised clustering algorithm was applied to divide patients into subgroups according to the PM-related genes. Results. We identified 10 genes (MMP12, TAC1, TSPYL5, PPP1R14A, TMSB15B, NPY1R, PCDH9, EPM2AIP1, TIG7, and DYNC1I1) for PMGC diagnosis. The OS rates between the high- and low-risk groups at 1-, 3-, and 5-years were significantly different in the training and validation sets. The AUCs at 1-, 3-, and 5-years in the training set were 0.71, 0.74, and 0.73, respectively. In the validation set, the AUCs at 1-, 3-, and 5-years were 0.68, 0.66, and 0.69, respectively. The 10 gene signatures were correlated with immune cell infiltration in both the high- and low-risk groups. In addition, based on the GSEA, several significant pathways were enriched in the high-risk PMGC group, such as the Wnt and transforming growth factor beta (TGF-β) signaling pathway and leukocyte transendothelial migration pathway. Furthermore, unsupervised cluster analysis showed that the model could distinguish the level of risk among patients with PMGC. Conclusions. Overall, 10 gene signatures were identified for PMGC risk prediction. These may be valuable in making clinical decisions to improve treatment outcomes in patients with PMGC.

Prognostic value of biglycan in gastric cancer

2020 ◽  
Author(s):  
Sizhe Hu ◽  
Peipei Li ◽  
Chenying Wang ◽  
Xiyong Liu
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Poor Prognosis ◽  
Prognostic Significance ◽  
Bioinformatic Analysis ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Mrna Levels ◽  
Gene Signatures ◽  
Kaplan Meier

Abstract Background: BGN (biglycan) is a family member of small leucine-rich repeat proteoglycans. High expression of BGN might enhance the invasion and metastasis in some types of tumors. Here, the prognostic significance of BGN was evaluated in gastric cancer.Material and Methods: Two independent Gene Expression Omnibus (GEO) gastric cancer microarray datasets( n= 64, n=432) were collected for this study. Kaplan-Meier analysis was applied to evaluate if BGN impacts the outcomes of gastric cancer. The gene set enrichment analysis (GSEA) was used to explore BGN and cancer-related gene signatures. Bioinformatic analysis predicted the putative transcription factors of BGN.Results: For gastric cancer, the mRNA expression level of BGN in tumor tissues was significantly higher than that in normal tissues. Kaplan-Meier analysis showed that higher expression of BGN mRNA was significantly associated with more reduced recurrence-free survival (RFS). GSEA results suggested that BGN significantly enriched metastasis and poor prognosis gene signatures, revealing that BGN might be associated with cell proliferation, poor differentiation, high invasiveness of gastric cancer. Meanwhile, the putative transcription factors, including AR, E2F1, and TCF4, weres predicted by bioinformatic analysis and also significantly correlated with expression of BGN in mRNA levels. Conclusion: High expression of BGN mRNA was significantly related to poor prognosis, which suggested BGN was a potential prognostic biomarker and therapeutic target of gastric cancer.

scholarly journals Prognostic value of gastric cancer-associated gene signatures: Evidence based on a meta-analysis using integrated bioinformatics methods

2018 ◽  
Vol 22 (11) ◽  
pp. 5743-5747 ◽  
Author(s):  
Jun Wang ◽  
Peng Gao ◽  
Yongxi Song ◽  
Jingxu Sun ◽  
Xiaowan Chen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Value ◽  
Meta Analysis ◽  
Evidence Based ◽  
Gene Signatures

Integration of multiple key molecules in lung adenocarcinoma identifies prognostic and immunotherapeutic relevant gene signatures

2020 ◽  
Vol 83 ◽  
pp. 106477 ◽  
Author(s):  
Qiong Wu ◽  
Lei Wang ◽  
Huagen Wei ◽  
Ben Li ◽  
Jiaming Yang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Gene Signatures ◽  
Relevant Gene

scholarly journals Identification of Gene Signatures Used to Recognize Biological Characteristics of Gastric Cancer upon Gene Expression Data

2014 ◽  
Vol 9 ◽  
pp. BMI.S13059 ◽  
Author(s):  
Zhi Yan ◽  
Brian T. Luke ◽  
Shirley X. Tsang ◽  
Rui Xing ◽  
Yuanming Pan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Information Gain ◽  
Small Sample Size ◽  
High Sensitivity ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Biological Characteristics ◽  
Gene Signatures ◽  
Mining Model

High-throughput gene expression microarrays can be examined by machine-learning algorithms to identify gene signatures that recognize the biological characteristics of specific human diseases, including cancer, with high sensitivity and specificity. A previous study compared 20 gastric cancer (GC) samples against 20 normal tissue (NT) samples and identified 1,519 differentially expressed genes (DEGs). In this study, Classification Information Index (CII), Information Gain Index (IGI), and RELIEF algorithms are used to mine the previously reported gene expression profiling data. In all, 29 of these genes are identified by all three algorithms and are treated as GC candidate biomarkers. Three biomarkers, COL1A2, ATP4B, and HADHSC, are selected and further examined using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining in two independent sets of GC and normal adjacent tissue (NAT) samples. Our study shows that COL1A2 and HADHSC are the two best biomarkers from the microarray data, distinguishing all GC from the NT, whereas ATP4B is diagnostically significant in lab tests because of its wider range of fold-changes in expression. Herein, a data-mining model applicable for small sample sizes is presented and discussed. Our result suggested that this mining model may be useful in small sample-size studies to identify putative biomarkers and potential biological features of GC.

scholarly journals Association between Morphologic CT Imaging Traits and Prognostically Relevant Gene Signatures in Women with High-Grade Serous Ovarian Cancer: A Hypothesis-generating Study

Radiology ◽  
2015 ◽  
Vol 274 (3) ◽  
pp. 742-751 ◽  
Author(s):  
Hebert Alberto Vargas ◽  
Maura Miccò ◽  
Seong Im Hong ◽  
Debra A. Goldman ◽  
Fanny Dao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Ct Imaging ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Gene Signatures ◽  
Relevant Gene
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