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Co-expression Network Analysis Identifies Fourteen Hub Genes Associated with Prognosis in Clear Cell Renal Cell Carcinoma

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Summary

Renal cancer is a common genitourinary malignance, of which clear cell renal cell carcinoma (ccRCC) has high aggressiveness and leads to most cancer-related deaths. Identification of sensitive and reliable biomarkers for predicting tumorigenesis and progression has great significance in guiding the diagnosis and treatment of ccRCC. Here, we identified 2397 common differentially expressed genes (DEGs) using paired normal and tumor ccRCC tissues from GSE53757 and The Cancer Genome Atlas (TCGA). Then, we performed weighted gene co-expression network analysis and protein-protein interaction network analysis, 17 candidate hub genes were identified. These candidate hub genes were further validated in GSE36895 and Oncomine database and 14 real hub genes were identified. All the hub genes were up-regulated and significantly positively correlated with pathological stage and histologic grade of ccRCC. Survival analysis showed that the higher expression level of each hub gene tended to predict a worse clinical outcome. ROC analysis showed that all the hub genes can accurately distinguish between tumor and normal samples, and between early stage and advanced stage ccRCC. Moreover, all the hub genes were positively associated with distant metastasis, lymph node infiltration, tumor recurrence and the expression of MKi67, suggesting these genes might promote tumor proliferation, invasion and metastasis. Furthermore, the functional annotation demonstrated that most genes were enriched in cell-cycle related biological function. In summary, our study identified 14 potential biomarkers for predicting tumorigenesis and progression, which might contribute to early diagnosis, prognosis prediction and therapeutic intervention.

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References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin, 2019,69(1):7–34

    Article  Google Scholar 

  2. Ljungberg B, Bensalah K, Canfield S, et al. EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol, 2015,67(5):913–924

    Article  PubMed  Google Scholar 

  3. Pierorazio PM, Johnson MH, Patel HD, et al. Management of Renal Masses and Localized Renal Cancer: Systematic Review and Meta-Analysis. J Urol, 2016,196(4):989–999

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bazzi WM, Sjoberg DD, Feuerstein MA, et al. Long-Term Survival Rates after Resection for Locally Advanced Kidney Cancer: Memorial Sloan Kettering Cancer Center 1989 to 2012 Experience. J Urol, 2015,193(6):1911–1916

    Article  PubMed  Google Scholar 

  5. Hsieh JJ, Purdue MP, Signoretti S, et al. Renal cell carcinoma. Nat Rev Dis Primers, 2017,3:17009

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhao E, Li L, Zhang W, et al. Comprehensive characterization of immune- and inflammation-associated biomarkers based on multi-omics integration in kidney renal clear cell carcinoma. J Transl Med, 2019,17(1):177

    Article  PubMed  PubMed Central  Google Scholar 

  7. Guan LY, Tan JF, Li H, et al. Biomarker identification in clear cell renal cell carcinoma based on miRNA-seq and digital gene expression-seq data. Gene, 2018,647:205–212

    Article  CAS  PubMed  Google Scholar 

  8. Tavazoie S, Hughes JD, Campbell MJ, et al. Systematic determination of genetic network architecture. Nat Genet, 1999,22(3):281–285

    Article  CAS  PubMed  Google Scholar 

  9. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008,9:559

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Clarke C, Madden SF, Doolan P, et al. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis, 2013,34(10):2300–2308

    Article  CAS  PubMed  Google Scholar 

  11. Wang L, Tang H, Thayanithy V, et al. Gene networks and microRNAs implicated in aggressive prostate cancer. Cancer Res, 2009,69(24):9490–9497

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gu HY, Yang M, Guo J, et al. Identification of the Biomarkers and Pathological Process of Osteoarthritis: Weighted Gene Co-expression Network Analysis. Front Physiol, 2019,10:275

    Article  PubMed  PubMed Central  Google Scholar 

  13. Chen L, Yuan L, Qian K, et al. Identification of Biomarkers Associated With Pathological Stage and Prognosis of Clear Cell Renal Cell Carcinoma by Coexpression Network Analysis. Front Physiol, 2018,9:399

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ficarra V, Schips L, Guille F, et al. Multiinstitutional European validation of the 2002 TNM staging system in conventional and papillary localized renal cell carcinoma. Cancer, 2005,104(5):968–974

    Article  PubMed  Google Scholar 

  15. Horvath S, Dong J. Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol, 2008,4(8):e1000117

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009,4(1):44–57

    Article  CAS  Google Scholar 

  17. Goel MK, Khanna P, Kishore J. Understanding survival analysis: Kaplan-Meier estimate. Int J Ayurveda Res, 2010,1(4):274–278

    Article  PubMed  PubMed Central  Google Scholar 

  18. Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res, 2017,45(W1):W98–W102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sing T, Sander O, Beerenwinkel N, et al. ROCR: visualizing classifier performance in R. Bioinformatics, 2005,21(20):3940–3941

    Article  CAS  PubMed  Google Scholar 

  20. Gayed BA, Youssef RF, Bagrodia A, et al. Ki67 is an independent predictor of oncological outcomes in patients with localized clear-cell renal cell carcinoma. BJU Int, 2014,113(4):668–673

    Article  PubMed  Google Scholar 

  21. Tilki D, Nguyen HG, Dall’Era MA, et al. Impact of histologic subtype on cancer-specific survival in patients with renal cell carcinoma and tumor thrombus. Eur Urol, 2014,66(3):577–583

    Article  PubMed  Google Scholar 

  22. Gospodarowicz MK, Miller D, Groome PA, et al. The process for continuous improvement of the TNM classification. Cancer, 2004,100(1):1–5

    Article  PubMed  Google Scholar 

  23. Xu J, Latif S, Wei S. Metastatic renal cell carcinoma presenting as gastric polyps: A case report and review of the literature. Int J Surg Case Rep, 2012,3(12):601–604

    Article  PubMed  PubMed Central  Google Scholar 

  24. Frank I, Blute ML, Cheville JC, et al. An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. J Urol, 2002,168(6):2395–400

    Article  PubMed  Google Scholar 

  25. Chen L, Yuan LS, Wang YZ, et al. Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma. Int J Biol Sci, 2017,13(11):1361–1372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yuan L, Chen L, Qian K, et al. Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC). Genom Data, 2017,14:132–140

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wang Y, Chen L, Wang G, et al. Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis. J Cell Physiol, 2019,234(7):10225–10237

    Article  CAS  PubMed  Google Scholar 

  28. Dachineni R, Ai GQ, Kumar DR, et al. Cyclin A2 and CDK2 as Novel Targets of Aspirin and Salicylic Acid: A Potential Role in Cancer Prevention. Mol Cancer Res, 2016,14(3):241–252

    Article  CAS  PubMed  Google Scholar 

  29. Lei CY, Wang W, Zhu YT, et al. The decrease of cyclin B2 expression inhibits invasion and metastasis of bladder cancer. Urol Oncol, 2016,34(5):237.e1–10

    Article  CAS  Google Scholar 

  30. Mo ML, Chen Z, Li J, et al. Use of serum circulating CCNB2 in cancer surveillance. Int J Biol Markers, 2010,25(4):236–242

    Article  CAS  PubMed  Google Scholar 

  31. Duxbury MS, Whang EE. RRM2 induces NF-kappaB-dependent MMP-9 activation and enhances cellular invasiveness. Biochem Biophys Res Commun, 2007,354(1):190–196

    Article  CAS  PubMed  Google Scholar 

  32. Zhang K, Hu S, Wu J, et al. Overexpression of RRM2 decreases thrombspondin-1 and increases VEGF production in human cancer cells in vitro and in vivo: implication of RRM2 in angiogenesis. Mol Cancer, 2009,8:11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Liu X, Zhou B, Xue L, et al. Ribonucleotide reductase subunits M2 and p53R2 are potential biomarkers for metastasis of colon cancer. Clin Colorectal Cancer, 2007,6(5):374–381

    Article  CAS  PubMed  Google Scholar 

  34. Gartel AL. FOXM1 in Cancer: Interactions and Vulnerabilities. Cancer Res, 2017,77(12):3135–3139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Koo CY, Muir KW, Lam EW. FOXM1: From cancer initiation to progression and treatment. Biochim Biophys Acta, 2012,1819(1):28–37

    Article  CAS  PubMed  Google Scholar 

  36. Guo L, Ding Z, Huang N, et al. Forkhead Box M1 positively regulates UBE2C and protects glioma cells from autophagic death. Cell Cycle, 2017,16(18):1705–1718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hu GH, Yan ZW, Zhang C, et al. FOXM1 promotes hepatocellular carcinoma progression by regulating KIF4A expression. J Exp Clin Cancer Res, 2019,38

  38. Khongkow P, Gomes AR, Gong C, et al. Paclitaxel targets FOXM1 to regulate KIF20A in mitotic catastrophe and breast cancer paclitaxel resistance. Oncogene, 2016,35(8):990–1002

    Article  CAS  PubMed  Google Scholar 

  39. Kalimutho M, Sinha D, Jeffery J, et al. CEP55 is a determinant of cell fate during perturbed mitosis in breast cancer. EMBO Mol Med, 2018,10(9)

  40. Li Y, Zhang ZF, Chen JD, et al. VX680/MK-0457, a potent and selective Aurora kinase inhibitor, targets both tumor and endothelial cells in clear cell renal cell carcinoma. Am J Transl Res, 2010,2(3):296–308

    CAS  PubMed  PubMed Central  Google Scholar 

  41. van Gijn SE, Wierenga E, van den Tempel N, et al. TPX2/Aurora kinase A signaling as a potential therapeutic target in genomically unstable cancer cells. Oncogene, 2019,38(6):852–867

    Article  CAS  PubMed  Google Scholar 

  42. Hu P, Chen X, Sun J, et al. siRNA-mediated knockdown against NUF2 suppresses pancreatic cancer proliferation in vitro and in vivo. Biosci Rep, 2015,35(1):e00170

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Fu HL, Shao L. Silencing of NUF2 inhibits proliferation of human osteosarcoma Saos-2 cells. Eur Rev Med Pharmacol Sci, 2016,20(6):1071–1079

    PubMed  Google Scholar 

  44. Ghaffari K, Hashemi M, Ebrahimi E, et al. BIRC5 Genomic Copy Number Variation in Early-Onset Breast Cancer. Iran Biomed J, 2016,20(4):241–245

    PubMed  PubMed Central  Google Scholar 

  45. Ren Q, Jin B. The clinical value and biological function of PTTG1 in colorectal cancer. Biomed Pharmacother, 2017,89:108–115

    Article  CAS  PubMed  Google Scholar 

  46. Zhang Q, Su RX, Shan C, et al. Non-SMC Condensin I Complex, Subunit G (NCAPG) is a Novel Mitotic Gene Required for Hepatocellular Cancer Cell Proliferation and Migration. Oncol Res, 2018,26(2):269–276

    Article  PubMed  PubMed Central  Google Scholar 

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Author notes

  1. The authors contributed equally to this work.

Authors and Affiliations

  1. Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China

    Jia-yi Chen, Yan Sun, Nan Qiao, Yang-yang Ge, Jian-hua Li, Yun Lin & Shang-long Yao

  2. Institute of Anesthesia and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China

    Jia-yi Chen, Yan Sun, Nan Qiao, Yang-yang Ge, Jian-hua Li, Yun Lin & Shang-long Yao

Authors
  1. Jia-yi Chen
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  2. Yan Sun
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  3. Nan Qiao
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  4. Yang-yang Ge
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  5. Jian-hua Li
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  6. Yun Lin
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Correspondence to Yun Lin or Shang-long Yao.

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The authors declare that there is no conflict of interest with any financial organization or corporation or individual that can inappropriately influence this work.

Additional information

This work was supported by grants from the National Natural Science Foundation of China (No. 81270354) and Natural Science for Youth Foundation (No. 81300213).

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Chen, Jy., Sun, Y., Qiao, N. et al. Co-expression Network Analysis Identifies Fourteen Hub Genes Associated with Prognosis in Clear Cell Renal Cell Carcinoma. CURR MED SCI 40, 773–785 (2020). https://doi.org/10.1007/s11596-020-2245-6

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  • DOI: https://doi.org/10.1007/s11596-020-2245-6

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