Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Aug 2:10:e13711.
doi: 10.7717/peerj.13711. eCollection 2022.

Differential gene expression analysis after DAPK1 knockout in hepatocellular carcinoma cells

Yuanqi Li #  1 , Hui Huang #  1 , Huajun Yu  1 , Ting Mo  1 , Ting Wei  1 , Guodan Li  1 , Yufang Jia  1 , Xiaoqin Huang  1 , Mingjin Tu  1 , Xiuwen Yan  1 , Haitao Zhang  1
Affiliations

Differential gene expression analysis after DAPK1 knockout in hepatocellular carcinoma cells

Yuanqi Li et al. PeerJ. .

Abstract

Background: The mechanism through which death-associated protein kinase 1 (DAPK1) causes hepatocellular carcinoma (HCC) progression remains unclear. In this study, we aimed to identify key proteins that were altered after DAPK1 knockout.

Methods: Stable DAPK1 knockout HCC cell lines were established, then the differentially expressed genes (DEGs) of HCC were screened using the NetworkAnalyst database and enriched using the Metascape software. Protein-protein interaction networks (PPIs) were analyzed and visualized using the STRING database expansion.

Results: In total, 732 differentially expressed genes were identified, including 415 upregulated genes and 317 downregulated genes. Through Cytoscape software scoring, 10 pivotal genes were found to be closely related to changes in DAPK1 expression; Kininogen-1 (KNG1), Complement C3 (C3), Metalloproteinase inhibitor 1 (TIMP1), and Alpha-2-HS-glycoprotein (AHSG) were the most strongly associated with DAPK1 expression changes. Moreover, western blot analysis results revealed that changes in the levels of proteins encoded by the four key genes after DAPK1 knockout were consistent with those seen in the database screening.

Conclusions: These results provide a direction for further studies on the DAPK1 gene and on the mechanism through which DAPK1 leads to hepatocellular carcinoma development.

Keywords: Bioinformatics analysis; DAPK1; Differential gene; Hepatocellular carcinoma; Related gene.

PubMed Disclaimer

Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Establishing stable DAPK1-knockout cell lines.
After Cas9-DAPK1 plasmid PLC/PRF/5 cell transfection, DAPK1 expression was detected by western blot. PLC, PLC/PRF/5 cells; KO, DAPK1-knockout PLC/PRF/5 cells.
Figure 2
Figure 2. Differential expression of DAPK1 and volcano plot and heat maps of differential genes.
(A) Relative expression of DAPK1 compared to normal liver tissue samples. (B, C) Volcano plot and heat map showing the 732 differentially expressed genes. The color red indicates upregulated genes, and blue indicates downregulated genes. PLC, PLC/PRF/5 cells; KO, DAPK1-knockout PLC/PRF/5 cells. padj <0.5, logFC >1.
Figure 3
Figure 3. Enrichment analysis of differentially expressed genes (DEGs).
(A, B) Functional enrichment analysis of DEGs. Bar graph showing the top 20 results from enrichment analyses of upregulated and downregulated genes.. P value is shown in color. (C, D) The network of enriched terms of upregulated and downregulated genes, showing the top 20. Each cluster ID is indicated with a specific color.
Figure 4
Figure 4. PPI network construction and module analysis.
(A) PPI network of DEGs. The upregulated genes are marked in red, while the downregulated genes are marked in blue; (B) the densest connected regions (24 nodes, 276 edges) in the PPI network were identified using Cytoscape. (C) Ten hub genes were identified in the densest connected regions with the MCC algorithm, using cytoHubba. The score is indicated in the color red. A darker color indicates a higher score.
Figure 5
Figure 5. Comparison of gene expression and protein expression of four hub genes.
(A, B, C, D) Relative expression of KNG1, C3, TIMP1, and AHSG in HCC, compared to normal liver tissue samples. ns, p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, P < 0.0001. On the left is the mRNA expression levels of KNG1, C3, TIMP1, and AHSG, and on the right is the protein expression levels. (E) Validation of four genes in PLC/PRF/5 cells and DAPK1-knockout PLC/PRF/5 cells. PLC, PLC/PRF/5 cells; KO, DAPK1-knockout PLC/PRF/5 cells.
Figure 6
Figure 6. Results of animal experiments.
(A) Growth curves of two stable cell lines. (B) The image of dissected tumors from nude mice. (C) Tumor weight histogram from nude mice. (D) Tumor volume growth curves in nude mice. PLC, PLC/PRF/5 cells; KO, DAPK1-knockout PLC/PRF/5 cells.
See this image and copyright information in PMC

References

    1. Ali OS, Abo-Shadi MA, Hammad LN. The biological significance of serum complements C3 and C4 in HCV-related chronic liver diseases and hepatocellular carcinoma. Egyptian journal of Immunology. 2005;12:91–99. - PubMed
    1. Anwanwan D, Singh SK, Singh S, Saikam V, Singh R. Challenges in liver cancer and possible treatment approaches. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 2020;1873:188314. doi: 10.1016/j.bbcan.2019.188314. - DOI - PMC - PubMed
    1. Bodoor K, Haddad Y, Alkhateeb A, Al-Abbadi A, Dowairi M, Magableh A, Bsoul N, Ghabkari A. DNA hypermethylation of cell cycle (p15 and p16) and apoptotic (p14, p53, DAPK and TMS1) genes in peripheral blood of leukemia patients. Asian Pacific Journal of Cancer Prevention. 2014;15:75–84. doi: 10.7314/apjcp.2014.15.1.75. - DOI - PubMed
    1. Chen J, Wu W, Chen L, Ma X, Zhao Y, Zhou H, Yang R, Hu L. Expression and clinical significance of AHSG and complement C3 in pancreatic ductal adenocarcinoma. Zhonghua Yi Xue Za Zhi. 2014;94:2175–2179. - PubMed
    1. Evers B, Jastrzebski K, Heijmans JP, Grernrum W, Beijersbergen RL, Bernards R. CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nature Biotechnology. 2016;34:631–633. doi: 10.1038/nbt.3536. - DOI - PubMed

Publication types

MeSH terms

Substances