scholarly journals The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data

2017 ◽  
Vol 34 (6) ◽  
Author(s):  
Chaoyang Zhang ◽  
Li Peng ◽  
Yaqin Zhang ◽  
Zhaoyang Liu ◽  
Wenling Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Throughput ◽  
Bioinformatics Analysis ◽  
High Throughput Data ◽  
Key Genes

The identification of key genes in nasopharyngeal carcinoma by bioinformatics analysis of high-throughput data

2019 ◽  
Vol 46 (3) ◽  
pp. 2829-2840 ◽  
Author(s):  
Yanshan Ge ◽  
Zhengxi He ◽  
Yanqi Xiang ◽  
Dawei Wang ◽  
Yuping Yang ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
High Throughput ◽  
Bioinformatics Analysis ◽  
High Throughput Data ◽  
Key Genes

scholarly journals Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods

2021 ◽  
Vol 14 (S1) ◽  
Author(s):  
Zishuang Zhang ◽  
Zhi-Ping Liu
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Feature Selection ◽  
High Throughput ◽  
Biomarker Discovery ◽  
Selection Process ◽  
Recursive Feature Elimination ◽  
Statistical Interpretation ◽  
Cancer Data ◽  
High Throughput Data

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common cancers. The discovery of specific genes severing as biomarkers is of paramount significance for cancer diagnosis and prognosis. The high-throughput omics data generated by the cancer genome atlas (TCGA) consortium provides a valuable resource for the discovery of HCC biomarker genes. Numerous methods have been proposed to select cancer biomarkers. However, these methods have not investigated the robustness of identification with different feature selection techniques. Methods We use six different recursive feature elimination methods to select the gene signiatures of HCC from TCGA liver cancer data. The genes shared in the six selected subsets are proposed as robust biomarkers. Akaike information criterion (AIC) is employed to explain the optimization process of feature selection, which provides a statistical interpretation for the feature selection in machine learning methods. And we use several methods to validate the screened biomarkers. Results In this paper, we propose a robust method for discovering biomarker genes for HCC from gene expression data. Specifically, we implement recursive feature elimination cross-validation (RFE-CV) methods based on six different classication algorithms. The overlaps in the discovered gene sets via different methods are referred as the identified biomarkers. We give an interpretation of the feature selection process based on machine learning using AIC in statistics. Furthermore, the features selected by the backward logistic stepwise regression via AIC minimum theory are completely contained in the identified biomarkers. Through the classification results, the superiority of interpretable robust biomarker discovery method is verified. Conclusions It is found that overlaps among gene subsets contain different quantitative features selected by the RFE-CV of 6 classifiers. The AIC values in the model selection provide a theoretical foundation for the feature selection process of biomarker discovery via machine learning. What’s more, genes containing in more optimally selected subsets make better biological sense and implication. The quality of feature selection is improved by the intersections of biomarkers selected from different classifiers. This is a general method suitable for screening biomarkers of complex diseases from high-throughput data.

scholarly journals Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8930 ◽  
Author(s):  
Xi Ma ◽  
Lin Zhou ◽  
Shusen Zheng
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Survival Analysis ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Key Genes ◽  
Overall Survival Analysis

Background Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. However, the molecular mechanisms involved in HCC remain unclear and are in urgent need of elucidation. Therefore, we sought to identify biomarkers in the prognosis of HCC through an integrated bioinformatics analysis. Methods Messenger RNA (mRNA) expression profiles were obtained from the Gene Expression Omnibus database and The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) for the screening of common differentially expressed genes (DEGs). Function and pathway enrichment analysis, protein-protein interaction network construction and key gene identification were performed. The significance of key genes in HCC was validated by overall survival analysis and immunohistochemistry. Meanwhile, based on TCGA data, prognostic microRNAs (miRNAs) were decoded using univariable and multivariable Cox regression analysis, and their target genes were predicted by miRWalk. Results Eleven hub genes (upregulated ASPM, AURKA, CCNB2, CDC20, PRC1 and TOP2A and downregulated AOX1, CAT, CYP2E1, CYP3A4 and HP) with the most interactions were considered as potential biomarkers in HCC and confirmed by overall survival analysis. Moreover, AURKA, PRC1, TOP2A, AOX1, CYP2E1, and CYP3A4 were considered candidate liver-biopsy markers for high risk of developing HCC and poor prognosis in HCC. Upregulation of hsa-mir-1269b, hsa-mir-518d, hsa-mir-548aq, hsa-mir-548f-1, and hsa-mir-6728, and downregulation of hsa-mir-139 and hsa-mir-4800 were determined to be risk factors of poor prognosis, and most of these miRNAs have strong potential to help regulate the expression of key genes. Conclusions This study undertook the first large-scale integrated bioinformatics analysis of the data from Illumina BeadArray platforms and the TCGA database. With a comprehensive analysis of transcriptional alterations, including mRNAs and miRNAs, in HCC, our study presented candidate biomarkers for the surveillance and prognosis of the disease, and also identified novel therapeutic targets at the molecular and pathway levels.

Identification of Potential Key Genes for Hepatitis B Virus-Associated Hepatocellular Carcinoma by Bioinformatics Analysis

2019 ◽  
Vol 26 (5) ◽  
pp. 485-494 ◽  
Author(s):  
Zide Chen ◽  
Jiehua Chen ◽  
Xuan Huang ◽  
Yi Wu ◽  
Kuiyuan Huang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Bioinformatics Analysis ◽  
B Virus ◽  
Key Genes
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