scholarly journals Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer

2022 ◽  
Vol 9 ◽  
Author(s):  
Tingna Chen ◽  
Qiuming He ◽  
Zhenxian Xiang ◽  
Rongzhang Dou ◽  
Bin Xiong
Keyword(s):  
Gastric Cancer ◽  
Regulatory Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
R Package ◽  
Normal Tissues ◽  
Gene Pairs ◽  
Aggressive Cancer ◽  
Gene Modules ◽  
Key Genes

Background: Gastric cancer (GC) is aggressive cancer with a poor prognosis. Previously bulk transcriptome analysis was utilized to identify key genes correlated with the development, progression and prognosis of GC. However, due to the complexity of the genetic mutations, there is still an urgent need to recognize core genes in the regulatory network of GC.Methods: Gene expression profiles (GSE66229) were retrieved from the GEO database. Weighted correlation network analysis (WGCNA) was employed to identify gene modules mostly correlated with GC carcinogenesis. R package ‘DiffCorr’ was applied to identify differentially correlated gene pairs in tumor and normal tissues. Cytoscape was adopted to construct and visualize the gene regulatory network.Results: A total of 15 modules were detected in WGCNA analysis, among which three modules were significantly correlated with GC. Then genes in these modules were analyzed separately by “DiffCorr”. Multiple differentially correlated gene pairs were recognized and the network was visualized by the software Cytoscape. Moreover, GEMIN5 and PFDN2, which were rarely discussed in GC, were identified as key genes in the regulatory network and the differential expression was validated by real-time qPCR, WB and IHC in cell lines and GC patient tissues.Conclusions: Our research has shed light on the carcinogenesis mechanism by revealing differentially correlated gene pairs during transition from normal to tumor. We believe the application of this network-based algorithm holds great potential in inferring relationships and detecting candidate biomarkers.

scholarly journals Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽  
2002 ◽  
Vol 21 (42) ◽  
pp. 6549-6556 ◽  
Author(s):  
Jiafu Ji ◽  
Xin Chen ◽  
Suet Yi Leung ◽  
Jen-Tsan A Chi ◽  
Kent Man Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis ◽  
Human Gastric Cancer ◽  
Gastric Cancer Cell Lines

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

scholarly journals Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Shenglan Cai ◽  
Xingwang Hu ◽  
Ruochan Chen ◽  
Yiya Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Immune Genes ◽  
Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

Cancer classification of single-cell gene expression data by neural network

2019 ◽  
Author(s):  
Bong-Hyun Kim ◽  
Kijin Yu ◽  
Peter C W Lee
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification ◽  
Supplementary Information ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Normal Tissues

Abstract Motivation Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). Results We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples. Availability and implementation Cancer classification by neural network. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Comparative Transcriptome Analysis of Different Dendrobium Species Reveals Active Ingredients-Related Genes and Pathways

2020 ◽  
Vol 21 (3) ◽  
pp. 861 ◽  
Author(s):  
Yingdan Yuan ◽  
Bo Zhang ◽  
Xinggang Tang ◽  
Jinchi Zhang ◽  
Jie Lin
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Active Ingredients ◽  
Hub Genes ◽  
Transcriptome Database ◽  
Gene Modules ◽  
Functional Investigation ◽  
First Time

Dendrobium is widely used in traditional Chinese medicine, which contains many kinds of active ingredients. In recent years, many Dendrobium transcriptomes have been sequenced. Hence, weighted gene co-expression network analysis (WGCNA) was used with the gene expression profiles of active ingredients to identify the modules and genes that may associate with particular species and tissues. Three kinds of Dendrobium species and three tissues were sampled for RNA-seq to generate a high-quality, full-length transcriptome database. Based on significant changes in gene expression, we constructed co-expression networks and revealed 19 gene modules. Among them, four modules with properties correlating to active ingredients regulation and biosynthesis, and several hub genes were selected for further functional investigation. This is the first time the WGCNA method has been used to analyze Dendrobium transcriptome data. Further excavation of the gene module information will help us to further study the role and significance of key genes, key signaling pathways, and regulatory mechanisms between genes on the occurrence and development of medicinal components of Dendrobium.

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