A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

Abstract To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expressio...

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Autores principales:	Yan Zhang, Zhengkui Lin, Xiaofeng Lin, Xue Zhang, Qian Zhao, Yeqing Sun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b3c3482223984a35ac45df06d4aacdb0
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spelling	oai:doaj.org-article:b3c3482223984a35ac45df06d4aacdb02021-12-02T13:33:52ZA gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma10.1038/s41598-021-84837-y2045-2322https://doaj.org/article/b3c3482223984a35ac45df06d4aacdb02021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84837-yhttps://doaj.org/toc/2045-2322Abstract To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.Yan ZhangZhengkui LinXiaofeng LinXue ZhangQian ZhaoYeqing SunNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Zhang Zhengkui Lin Xiaofeng Lin Xue Zhang Qian Zhao Yeqing Sun A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma
description	Abstract To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.
format	article
author	Yan Zhang Zhengkui Lin Xiaofeng Lin Xue Zhang Qian Zhao Yeqing Sun
author_facet	Yan Zhang Zhengkui Lin Xiaofeng Lin Xue Zhang Qian Zhao Yeqing Sun
author_sort	Yan Zhang
title	A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma
title_short	A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma
title_full	A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma
title_fullStr	A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma
title_full_unstemmed	A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma
title_sort	gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b3c3482223984a35ac45df06d4aacdb0
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A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

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