Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

Abstract

This study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

Ursolic Acid Regulates Cell Cycle and Proliferation in Colon Adenocarcinoma by Suppressing Cyclin B1

“…The visual network of molecular interactions was established by Cytoscape (version 3.7.1, www.cytoscape.org/ ). The plugin cytoHubba was taken advantage to identify rounding out the top 10 hub genes in PPI based on Maximal Clique Centrality (MCC) methods ( Li and Xu, 2019 ). To further investigate the molecular mechanisms of CCNB1 in COAD, the Database for Annotation, Visualization, and Integrated Discovery database (DAVID, http://david.ncifcrf.gov , version 6.8) was used ( Huang et al, 2009 ).…”

Identification of UBE2C as hub gene in driving prostate cancer by integrated bioinformatics analysis

“…In order to screen the hub genes, the different expression genes were uploaded to STRING, then PPI network was further constructed by Cytoscape ( Fig 3 ). In order to identify the hub genes of prostate cancer, DEGs were calculated by Maximal Clique Centrality (MCC) [ 26 ], Degree method, Density of Maximum Neighborhood Component (DNMC) [ 22 ] to screen the top ten genes. The 7 genes, UBE2C, PBK, CCNB1, CDKN3, TOP2A, AURKA and MKI67, presenting in at least two methods, were recognized as candidate hub genes.…”

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

“…The feature weights are calculated based on the sample size and number of class labels. FSFS are tested for binary and multiclass datasets, but it is widely used for binary datasets [ 31 ]; hence, a suitable feature ranker is proposed for the current work. For a given set of features f ={ f 1 , f 2 , f 3 ,…, f p } having a set of classes K ={ k 1 , k 2 , k 3 ,…, k c }, the fisher score S of the feature f i can be estimated as follows: where n j is the number of instances in the j th class, μ i is the mean of the i th feature, and μ ij and ρ ij are the mean and variance of the i th feature and j th class, respectively.…”

“…e feature weights are calculated based on the sample size and number of class labels. FSFS are tested for binary and multiclass datasets, but it is widely used for binary datasets [31]; hence, a suitable feature ranker is proposed for the current work. For a given set of features f � f 1 , f 2 , f 3 , .…”