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Original Research

Open Access

Identification of potential key genes associated with termination phase of rat liver regeneration through microarray analysis

  • Haitham Salameen1,†
  • Menghao Wang1,†
  • Jianping Gong1

1Department of Hepatobiliary Surgery, Secondary Affiliated Hospital of Chongqing Medical University, 400010 Chongqing, China

DOI: 10.31083/jomh.2021.051

Submitted: 28 January 2021 Accepted: 12 March 2021

Online publish date: 18 June 2021

*Corresponding Author(s): Jianping Gong E-mail:

† These authors contributed equally.

PDF (1.98 MB) Supplementary material


Background and objective: Liver regeneration (LR) is a complex process influenced by various genes and pathways, the majority of the of research on LR focus on the initiation and proliferation phase while studies on termination phase is lacking. We aimed to identify potential genes and reveal the underlying the molecular mechanisms involved in the precise regulation of liver size during the termination phase of LR.

Materials and methods: We obtained the rat liver tissue gene datasets (GSE63742) collected following partial hepatectomy (PH) from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI), from which, this study screened the late stage LR samples (7 days post-PH) using the R/Bioconductor packages for the identification of differentially expressed genes (DEGs). Afterwards, we performed enrichment analysis using the database for annotation visualization and integrated discovery (DAVID) online tool. Moreover, the Search Tool for the Retrieval of Interacting proteins (STRING) database was employed to construct protein-protein interaction (PPI) networks based on those identified DEGs; the PPI network was then used by Cytoscape software to predict hub genes and nodes. Animal experimentation (Rat PH model) was performed to acquire liver tissues which were then used for western blot analysis to verify our results.

Results: The present study identified together 74 significant DEGs, among which, 51 showed up-regulation while 23 presented as down-regulated. As revealed by KEGG pathway enrichment analysis, DEGs were mostly related to pathways such as retinol metabolism, steroid hormone synthesis, transforming growth factor-β (TGF-β) and mitogen-activated protein kinase (MAPK) signaling. In addition, as suggested by GO enrichment analysis, DEGs were mostly related to the cyclooxygenase P450 pathway, negative regulation of Notch signaling pathway, aromatase activity, steroid hydroxylase activity, exosomes, and extracellular domain. Analyses based on STRING database and Cytoscape software identified genes like Ste2 and Btg2 as the hub genes in the termination stage LR. The obtained results were confirmed by Western blot analysis.

Conclusions: Taken together, the microarray analysis in this study suggests that DEGs such as Ste2 and Btg2 are the hub genes, which are associated with the regulation of termination stage LR, while the molecular mechanisms are possibly related to the MAPK and TGF-β signal transduction pathways.


Liver regeneration; Differentially expressed genes; Enrichment analysis; Protein-protein interaction networks

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Haitham Salameen,Menghao Wang,Jianping Gong. Identification of potential key genes associated with termination phase of rat liver regeneration through microarray analysis. Journal of Men's Health. 2021.doi:10.31083/jomh.2021.051.


[1] Higgins GM, Anderson RM. Experimental pathology of liver: restora-tion of liver of white rat following partial surgical removal. Archives of Pathology & Laboratory Medicine. 1931; 12: 186–202.

[2] Forbes SJ, Newsome PN. Liver regeneration-mechanisms and models to clinical application. Nature Reviews Gastroenterology & Hepatol-ogy. 2016; 13: 473–485.

[3] Fausto N, Campbell JS, Riehle KJ. Liver regeneration. Hepatology. 2006; 43: S45–S53.

[4] Mao SA, Glorioso JM, Nyberg SL. Liver regeneration. Translational Research. 2014; 163: 352–362.

[5] Brettschneider J, Collin F, Bolstad BM, Speed TP. Quality assessment for short oligonucleotide microarray data. Technometrics. 2008; 50: 241–264.

[6] Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015; 43: e47.

[7] Gautier L, Cope L, Bolstad BM, Irizarry RA. Affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004; 20: 307–315.

[8] Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols. 2009; 4: 44–57.

[9] Kanehisa M. Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research. 2000; 28: 27–30.

[10] Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics. 2000; 25: 25–29.

[11] Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research. 2003; 13: 2498–2504.

[12] Vella D, Zoppis I, Mauri G, Mauri P, Di Silvestre D. From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data. EURASIP Journal on Bioinformatics & Systems Biology. 2020; 2017: 6.

[13] Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010; 11: R106.

[14] Gandolfo LC, Speed TP. RLE plots: visualizing unwanted variation in high dimensional data. PLoS ONE. 2018; 13: e0191629.

[15] Fasold M, Binder H. Estimating RNA-quality using GeneChip mi-croarrays. BMC Genomics. 2013; 13: 186.

[16] Campbell JS, Argast GM, Yuen SY, Hayes B, Fausto N. Inactivation of p38 MAPK during liver regeneration. The International Journal of Biochemistry & Cell Biology. 2011; 43: 180–188.

[17] Tormos AM, Arduini A, Talens-Visconti R, del Barco Barrantes I, Nebreda AR, Sastre J. Liver-specific p38α deficiency causes reduced cell growth and cytokinesis failure during chronic biliary cirrhosis in mice. Hepatology. 2013; 57: 1950–1961.

[18] Fortier M, Cadoux M, Boussetta N, Pham S, Donné R, Couty JP, et al. Hepatospecific ablation of p38alpha MAPK governs liver regeneration through modulation of inflammatory response to CCl4-induced acute injury. Scientific Reports. 2019; 9: 14614.

[19] Chen Z, Wan L, Jin X, Wang W, Li D. Transforming growth factor-β signaling confers hepatic stellate cells progenitor features after partial hepatectomy. Journal of Cellular Physiology. 2019; 235: 2655–2667.

[20] Mehta KJ, Coombes JD, Briones-Orta M, Manka PP, Williams R, Patel VB, et al. Iron Enhances hepatic fibrogenesis and activates transforming growth factor-beta signaling in murine hepatic stellate cells. The American Journal of the Medical Sciences. 2018; 355: 183–190.

[21] Chen J, Zaidi S, Rao S, Chen JS, Phan L, Farci P, et al. Analysis of genomes and transcriptomes of hepatocellular carcinomas identifies mutations and gene expression changes in the transforming growth factor-beta pathway. Gastroenterology. 2018; 154: 195–210.

[22] Wang C, Qi R, Li N, Wang Z, An H, Zhang Q, et al. Notch1 signaling sensitizes tumor necrosis factor-related apoptosis-inducing ligand-induced apoptosis in human hepatocellular carcinoma cells by inhibiting Akt/Hdm2-mediated p53 degradation and up-regulating p53-dependent DR5 expression. The Journal of Biological Chemistry. 2009; 284: 16183–16190.

[23] Morell CM, Strazzabosco M. Notch signaling and new therapeutic options in liver disease. Journal of Hepatology. 2014; 60: 885–890.

[24] Zhang F, Zhang J, Li X, Li B, Tao K, Yue S. Notch signaling pathway regulates cell cycle in proliferating hepatocytes involved in liver regeneration. Journal of Gastroenterology and Hepatology. 2018; 33: 1538–1547.

[25] Balaphas A, Meyer J, Sadoul R, Morel P, Gonelle-Gispert C, Bühler LH. Extracellular vesicles: future diagnostic and therapeutic tools for liver disease and regeneration. Liver International. 2019; 39: 1801–1817.

[26] Rong X, Liu J, Yao X, Jiang T, Wang Y, Xie F. Human bone marrow mesenchymal stem cells-derived exosomes alleviate liver fibrosis through the Wnt/β-catenin pathway. Stem Cell Research & Therapy. 2019; 10: 98.

[27] Guo Y, Hu B, Huang H, Tsung A, Gaikwad NW, Xu M, et al. Estrogen sulfotransferase is an oxidative stress-responsive gene that gender-specifically affects liver ischemia/reperfusion injury. Journal of Biological Chemistry. 2015; 290: 14754–14764.

[28] Orrù C, Szydlowska M, Taguchi K, Zavattari P, Perra A, Yamamoto M, et al. Genetic inactivation of Nrf2 prevents clonal expansion of initiated cells in a nutritional model of rat hepatocarcinogenesis. Journal of Hepatology. 2018; 69: 635–643.

[29] Zhao X, Zhuo H. ECR-MAPK regulation in liver early development. BioMed Research International. 2015; 2014: 850802.

[30] Wang XJ, Chamberlain M, Vassieva O, Henderson CJ, Wolf CR. Rela-tionship between hepatic phenotype and changes in gene expression in cytochrome P450 reductase (POR) null mice. The Biochemical Journal. 2005; 388: 857–867.

[31] Huck I, Gunewardena S, Espanol-Suner R, Willenbring H, Apte U. Hepatocyte nuclear factor 4 alpha activation is essential for termination of liver regeneration in mice. Hepatology. 2019; 70: 666–681.

[32] Song G, Sharma AD, Roll GR, Ng R, Lee AY, Blelloch RH, et al. Mi-croRNAs control hepatocyte proliferation during liver regeneration. Hepatology. 2010; 51: 1735–1743.

[33] Xie Y, Du J, Liu Z, Zhang D, Yao X, Yang Y. MiR-6875-3p promotes the proliferation, invasion and metastasis of hepatocellular carcinoma via BTG2/FAK/Akt pathway. Journal of Experimental & Clinical Cancer Research. 2019; 38: 7.

[34] Jiang H, Zhu Y, Zhou Z, Xu J, Jin S, Xu K, et al. PRMT5 promotes cell proliferation by inhibiting BTG2 expression via the ERK signaling pathway in hepatocellular carcinoma. Cancer Medicine. 2018; 7: 869–882.

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