Identification of genes related to the characteristics of cancer stem cells in hepatocellular carcinoma by weighted gene co-expression network analysis_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

XU Helong ¹ , LI yan ^2,3 , REN Chenghui ¹ , SONG Xiaojing ^1,2,3,4,5,6 , ZHANG Lei ^2,3,4,5,6 ,  LI Xun ^1,2,3,4,5,6

1. The First Clinical Medical College, Lanzhou University, Lanzhou 730000, P. R. China;
2. Gansu Province Institute of Hepatopancreatobiliary, Lanzhou 730000, P. R. China;
3. Gansu Province Key Laboratory of Biotherapy and Regenerative Medicine, Lanzhou 730000, P. R. China;
4. Fifth Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, P. R. China;
5. Gansu Province International Science and Technology Cooperation Base of Prevention and Control of Tumors with High Incidence and Major Chronic Diseases, Lanzhou 730000, P. R. China;
6. Gansu Province Liver Center of Integrated Traditional Chinese and West Medicine, Lanzhou 730000, P. R. China;

Corresponding author：

LI Xun, Email: lxdr21@126.com

Keywords：

hepatocellular carcinoma; cancer stem cell; weighted gene co-expression network analysis; mRNAsi; the cancer genome atlas

DOI：

10.7507/1007-9424.202001067

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Objective To explore the expression of genes related to hepatocellular carcinoma (HCC) stem cells and their prognostic correlation by using weighted gene co-expression network analysis (WGCNA).Methods Firstly, the transcriptome sequencing (RNA-seq) and clinical data of HCC were downloaded from the public database the Cancer Genome Atlas (TCGA), and the mRNA expression-based stiffness index (mRNAsi) table of cancer stem cells was downloaded and sorted out to analyze the relationship between mRNAsi and pathological grade and prognosis of HCC. The mRNAsi of HCC was downloaded and the prognostic value of mRNAsi was discussed. Then we used WGCNA to screen the key modules related to liver cancer stem cells (LSCS). Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. The online database STRING was used to construct hub genes coding proteins interaction (PPI) network and screen key genes. Finally, the key genes were analyzed for expression differences and expression correlations. The online database Kaplan-Meier plotter was used for survival analysis and verified.Results mRNAsi was significantly upregulated in cancer tissues (P<0.001), and increased with the increase of pathological grade of HCC (P=0.001). The mortality rate of the higher mRNAsi group was higher than that of the lower mRNAsi group (P=0.006). GO analysis found that hub genes were mainly involved in biological processes, such as mitosis and DNA replication, and KEGG showed that hub genes were enriched in cell cycle, DNA mismatch repair, oocyte meiosis, and other signaling pathways. We screened 10 key genes (included CCNB1, CDC20, CDCA8, NDC80, KIF20A, TTK, CDC45, KIF15, MCM2, and NCAPG) related to mRNAsi of HCC based on WGCNA. The key genes were highly expressed in the tumor samples compared to the normal samples. In addition, there was a strong interaction between proteins of these key genes (P<0.05), a strong co-expression relationship at the transcriptional level, and all related to prognosis of HCC.Conclusions mRNAsi plays an important role in the occurrence and development of HCC. Ten key genes related to LSCS were screened, which may act as therapeutic targets for inhibiting the stem cell characteristics of HCC.

Citation： XU Helong, LI yan, REN Chenghui, SONG Xiaojing, ZHANG Lei, LI Xun. Identification of genes related to the characteristics of cancer stem cells in hepatocellular carcinoma by weighted gene co-expression network analysis. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2020, 27(5): 560-568. doi: 10.7507/1007-9424.202001067 Copy

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2.	Lacaze L, Scotté M. Surgical treatment of intra hepatic recurrence of hepatocellular carcinoma. World J Hepatol, 2015, 7(13): 1755-1760.
3.	Ikeda M, Morizane C, Ueno M, et al. Chemotherapy for hepatocellular carcinoma: current status and future perspectives. Jpn J Clin Oncol, 2018, 48(2): 103-114.
4.	Peitzsch C, Tyutyunnykova A, Pantel K, et al. Cancer stem cells: the root of tumor recurrence and metastases. Semin Cancer Biol, 2017, 44: 10-24.
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6.	Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9: 559.
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8.	Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019, 47(D1): D607-D613.
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10.	Lian Q, Wang S, Zhang G, et al. HCCDB: a database of hepatocellular carcinoma expression atlas. Genomics Proteomics Bioinformatics, 2018, 16(4): 269-275.
11.	Chang JC. Cancer stem cells: role in tumor growth, recurrence, metastasis, and treatment resistance. Medicine (Baltimore), 2016, 95(1 Suppl 1): S20-S25.
12.	Olmeda F, Ben Amar M. Clonal pattern dynamics in tumor: the concept of cancer stem cells. Sci Rep, 2019, 9(1): 15607.
13.	Reinemann DN, Sturgill EG, Das DK, et al. Collective force regulation in anti-parallel microtubule gliding by dimeric Kif15 Kinesin motors. Curr Biol, 2017, 27(18): 2810-2820.
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17.	Shi C, Huang D, Lu N, et al. Aberrantly activated Gli2-KIF20A axis is crucial for growth of hepatocellular carcinoma and predicts poor prognosis. Oncotarget, 2016, 7(18): 26206-26219.
18.	Li Q, Qiu J, Yang H, et al. Kinesin family member 15 promotes cancer stem cell phenotype and malignancy via reactive oxygen species imbalance in hepatocellular carcinoma. Cancer Lett, 2019, [Epub ahead of print].
19.	Lian YF, Li SS, Huang YL, et al. Up-regulated and interrelated expressions of GINS subunits predict poor prognosis in hepatocellular carcinoma. Biosci Rep, 2018, 38(6): BSR20181178.
20.	Nagahama Y, Ueno M, Miyamoto S, et al. PSF1, a DNA replication factor expressed widely in stem and progenitor cells, drives tumo-rigenic and metastatic properties. Cancer Res, 2010, 70(3): 1215-1224.
21.	Guan C, Li J, Sun D, et al. The structure and polymerase-recognition mechanism of the crucial adaptor protein AND-1 in the human replisome. J Biol Chem, 2017, 292(23): 9627-9636.
22.	Simon NE, Schwacha A. The Mcm2-7 replicative helicase: a pro-mising chemotherapeutic target. Biomed Res Int, 2014, 2014: 549719.
23.	Zhang HP, Li SY, Wang JP, et al. Clinical significance and biological roles of cyclins in gastric cancer. Onco Targets Ther, 2018, 11: 6673-6685.
24.	Zhang X, Zheng Q, Wang C, et al. CCDC106 promotes non-small cell lung cancer cell proliferation. Oncotarget, 2017, 8(16): 26662-26670 .
25.	Yuan LJ, Li JD, Zhang L, et al. SPAG5 upregulation predicts poor prognosis in cervical cancer patients and alters sensitivity to taxol treatment via the mTOR signaling pathway. Cell Death Dis, 2014, 5: e1247.
26.	Lee SH, Oh SY, Do SI, et al. SOX2 regulates self-renewal and tumorigenicity of stem-like cells of head and neck squamous cell carcinoma. Br J Cancer, 2014, 111(11): 2122-2130.
27.	Shen S, Kong J, Qiu Y, et al. Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis. J Cell Biochem, 2019, 120(6): 10069-10081.
28.	Cui F, Tang H, Tan J, et al. Spindle pole body component 25 regulates stemness of prostate cancer cells. Aging (Albany NY), 2018, 10(11): 3273-3282.
29.	Choi M, Min YH, Pyo J, et al. TC Mps1 12, a novel Mps1 inhibitor, suppresses the growth of hepatocellular carcinoma cells via the accumulation of chromosomal instability. Br J Pharmacol, 2017, 174(12): 1810-1825.
30.	Huang D, Gao Q, Guo L, et al. Isolation and identification of cancer stem-like cells in esophageal carcinoma cell lines. Stem Cells Dev, 2009, 18(3): 465-473.
31.	Zhou W, Yang Y, Gu Z, et al. ALDH1 activity identifies tumor-initiating cells and links to chromosomal instability signatures in multiple myeloma. Leukemia, 2014, 28(5): 1155-1158.
32.	Hu K, Law JH, Fotovati A, et al. Small interfering RNA library screen identified polo-like kinase-1 (PLK1) as a potential therapeutic target for breast cancer that uniquely eliminates tumor-initiating cells. Breast Cancer Res, 2012, 14(1): R22.
33.	Park JH, Chung S, Matsuo Y, et al. Development of small molecular compounds targeting cancer stem cells. Medchemcomm, 2016, 8(1): 73-80.
34.	Wang Y, Gao B, Tan PY, et al. Genome-wide CRISPR knockout screens identify NCAPG as an essential oncogene for hepato-cellular carcinoma tumor growth. FASEB J, 2019, 33(8): 8759-8770.
35.	Gong C, Ai J, Fan Y, et al. NCAPG promotes the proliferation of hepatocellular carcinoma through PI3K/AKT signaling. Onco Targets Ther, 2019, 12: 8537-8552.
36.	Pan S, Zhan Y, Chen X, et al. Identification of biomarkers for controlling cancer stem cell characteristics in bladder cancer by network analysis of transcriptome data stemness indices. Front Oncol, 2019, 9: 613.

1. Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet, 2018, 391(10127): 1301-1314.
2. Lacaze L, Scotté M. Surgical treatment of intra hepatic recurrence of hepatocellular carcinoma. World J Hepatol, 2015, 7(13): 1755-1760.
3. Ikeda M, Morizane C, Ueno M, et al. Chemotherapy for hepatocellular carcinoma: current status and future perspectives. Jpn J Clin Oncol, 2018, 48(2): 103-114.
4. Peitzsch C, Tyutyunnykova A, Pantel K, et al. Cancer stem cells: the root of tumor recurrence and metastases. Semin Cancer Biol, 2017, 44: 10-24.
5. Malta TM, Sokolov A, Gentles AJ, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell, 2018, 173(2): 338-354.
6. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9: 559.
7. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009, 4(1): 44-57.
8. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019, 47(D1): D607-D613.
9. Nagy Á, Lánczky A, Menyhárt O, et al. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep, 2018, 8(1): 9227.
10. Lian Q, Wang S, Zhang G, et al. HCCDB: a database of hepatocellular carcinoma expression atlas. Genomics Proteomics Bioinformatics, 2018, 16(4): 269-275.
11. Chang JC. Cancer stem cells: role in tumor growth, recurrence, metastasis, and treatment resistance. Medicine (Baltimore), 2016, 95(1 Suppl 1): S20-S25.
12. Olmeda F, Ben Amar M. Clonal pattern dynamics in tumor: the concept of cancer stem cells. Sci Rep, 2019, 9(1): 15607.
13. Reinemann DN, Sturgill EG, Das DK, et al. Collective force regulation in anti-parallel microtubule gliding by dimeric Kif15 Kinesin motors. Curr Biol, 2017, 27(18): 2810-2820.
14. Wu WD, Yu KW, Zhong N, et al. Roles and mechanisms of Kinesin-6 KIF20A in spindle organization during cell division. Eur J Cell Biol, 2019, 98(2-4): 74-80.
15. Sheng Y, Wang W, Hong B, et al. Upregulation of KIF20A correlates with poor prognosis in gastric cancer. Cancer Manag Res, 2018, 10: 6205-6216.
16. Shen T, Yang L, Zhang Z, et al. KIF20A affects the prognosis of bladder cancer by promoting the proliferation and metastasis of bladder cancer cells. Dis Markers, 2019, 2019: 4863182.
17. Shi C, Huang D, Lu N, et al. Aberrantly activated Gli2-KIF20A axis is crucial for growth of hepatocellular carcinoma and predicts poor prognosis. Oncotarget, 2016, 7(18): 26206-26219.
18. Li Q, Qiu J, Yang H, et al. Kinesin family member 15 promotes cancer stem cell phenotype and malignancy via reactive oxygen species imbalance in hepatocellular carcinoma. Cancer Lett, 2019, [Epub ahead of print].
19. Lian YF, Li SS, Huang YL, et al. Up-regulated and interrelated expressions of GINS subunits predict poor prognosis in hepatocellular carcinoma. Biosci Rep, 2018, 38(6): BSR20181178.
20. Nagahama Y, Ueno M, Miyamoto S, et al. PSF1, a DNA replication factor expressed widely in stem and progenitor cells, drives tumo-rigenic and metastatic properties. Cancer Res, 2010, 70(3): 1215-1224.
21. Guan C, Li J, Sun D, et al. The structure and polymerase-recognition mechanism of the crucial adaptor protein AND-1 in the human replisome. J Biol Chem, 2017, 292(23): 9627-9636.
22. Simon NE, Schwacha A. The Mcm2-7 replicative helicase: a pro-mising chemotherapeutic target. Biomed Res Int, 2014, 2014: 549719.
23. Zhang HP, Li SY, Wang JP, et al. Clinical significance and biological roles of cyclins in gastric cancer. Onco Targets Ther, 2018, 11: 6673-6685.
24. Zhang X, Zheng Q, Wang C, et al. CCDC106 promotes non-small cell lung cancer cell proliferation. Oncotarget, 2017, 8(16): 26662-26670 .
25. Yuan LJ, Li JD, Zhang L, et al. SPAG5 upregulation predicts poor prognosis in cervical cancer patients and alters sensitivity to taxol treatment via the mTOR signaling pathway. Cell Death Dis, 2014, 5: e1247.
26. Lee SH, Oh SY, Do SI, et al. SOX2 regulates self-renewal and tumorigenicity of stem-like cells of head and neck squamous cell carcinoma. Br J Cancer, 2014, 111(11): 2122-2130.
27. Shen S, Kong J, Qiu Y, et al. Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis. J Cell Biochem, 2019, 120(6): 10069-10081.
28. Cui F, Tang H, Tan J, et al. Spindle pole body component 25 regulates stemness of prostate cancer cells. Aging (Albany NY), 2018, 10(11): 3273-3282.
29. Choi M, Min YH, Pyo J, et al. TC Mps1 12, a novel Mps1 inhibitor, suppresses the growth of hepatocellular carcinoma cells via the accumulation of chromosomal instability. Br J Pharmacol, 2017, 174(12): 1810-1825.
30. Huang D, Gao Q, Guo L, et al. Isolation and identification of cancer stem-like cells in esophageal carcinoma cell lines. Stem Cells Dev, 2009, 18(3): 465-473.
31. Zhou W, Yang Y, Gu Z, et al. ALDH1 activity identifies tumor-initiating cells and links to chromosomal instability signatures in multiple myeloma. Leukemia, 2014, 28(5): 1155-1158.
32. Hu K, Law JH, Fotovati A, et al. Small interfering RNA library screen identified polo-like kinase-1 (PLK1) as a potential therapeutic target for breast cancer that uniquely eliminates tumor-initiating cells. Breast Cancer Res, 2012, 14(1): R22.
33. Park JH, Chung S, Matsuo Y, et al. Development of small molecular compounds targeting cancer stem cells. Medchemcomm, 2016, 8(1): 73-80.
34. Wang Y, Gao B, Tan PY, et al. Genome-wide CRISPR knockout screens identify NCAPG as an essential oncogene for hepato-cellular carcinoma tumor growth. FASEB J, 2019, 33(8): 8759-8770.
35. Gong C, Ai J, Fan Y, et al. NCAPG promotes the proliferation of hepatocellular carcinoma through PI3K/AKT signaling. Onco Targets Ther, 2019, 12: 8537-8552.
36. Pan S, Zhan Y, Chen X, et al. Identification of biomarkers for controlling cancer stem cell characteristics in bladder cancer by network analysis of transcriptome data stemness indices. Front Oncol, 2019, 9: 613.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Identification of genes related to the characteristics of cancer stem cells in hepatocellular carcinoma by weighted gene co-expression network analysis

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