Skip to main content

Advertisement

SpringerLink
Log in

Integrated bioinformatics analysis to identify the key gene associated with metastatic clear cell renal cell carcinoma

  • Original Paper
  • Published:
Medical Oncology Aims and scope Submit manuscript

Abstract

Metastasis of clear cell renal cell carcinoma (ccRCC) is a leading cause of death. The purpose of this research was to investigate the key gene in ccRCC tumor metastasis. Three microarray datasets (GSE22541, GSE85258, and GSE105261), which included primary and metastatic ccRCC tissues, were obtained from the Gene Expression Omnibus (GEO) database. Expression profiling and clinical data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) dataset. A total of 20 overlapping differentially expressed genes (DEGs) were identified using the R limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the DEGs were mainly enriched in tumor metastasis-related pathways. Gene expression analysis and survival analysis in the GEPIA2 database further identified the key gene HSD11B2. qRT-PCR result manifested that HSD11B2 level was significantly down-regulated in ccRCC tissues compared with adjacent normal tissues. ROC analysis showed that HSD11B2 exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Univariate and multivariate Cox regression analysis showed that HSD11B2 expression was an independent prognostic factor. To establish a nomogram combining HSD11B2 expression and clinical factors, and a new method for predicting the survival probability of ccRCC patients. Gene Set Enrichment Analysis (GSEA) enrichment results showed that low expression of HSD11B2 was mainly enriched in tumor signaling pathways and immune-related pathways. Immune analysis revealed a significant correlation between HSD11B2 and tumor immune infiltrates in ccRCC. This study suggests that HSD11B2 can serve as a potential biomarker and therapeutic target for ccRCC metastasis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article. All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Abbreviations

ccRCC:

Clear cell renal cell carcinoma

RCC:

Renal cell carcinoma

DEGs:

Differentially expressed genes

GEO:

Gene expression omnibus

GO:

Gene ontology

OS:

Overall survival

TCGA:

The cancer genome atlas

KEGG:

Kyoto encyclopedia of genes and genomes

qRT-PCR:

Quantitative real-time PCR

GSEA:

Gene set enrichment analysis

GEPIA2:

The gene expression profiling interactive analysis 2

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics. CA: Cancer J Clin. 2021;71(1):7–33.

    Google Scholar 

  2. Padala SA, Barsouk A, Thandra KC, Saginala K, Mohammed A, Vakiti A, Rawla P, Barsouk A. Epidemiology of renal cell carcinoma. World J Oncol. 2020;11(3):79–87.

    Article  Google Scholar 

  3. Linehan WM, Ricketts CJ. The cancer genome atlas of renal cell carcinoma: findings and clinical implications. Nat Rev Urol. 2019;16(9):539–52.

    Article  CAS  Google Scholar 

  4. Hsieh JJ, Purdue MP, Signoretti S, Swanton C, Albiges L, Schmidinger M, Heng DY, Larkin J, Ficarra V. Renal cell carcinoma. Nat Rev Dis Primers. 2017;3:17009.

    Article  Google Scholar 

  5. Gao S, Yan L, Zhang H, Fan X, Jiao X, Shao F. Identification of a metastasis-associated gene signature of clear cell renal cell carcinoma. Front Genet. 2020;11:603455.

    Article  CAS  Google Scholar 

  6. Lalani AKA, McGregor BA, Albiges L, Choueiri TK, Motzer R, Powles T, Wood C, Bex A. Systemic treatment of metastatic clear cell renal cell carcinoma in 2018: current paradigms, use of immunotherapy, and future directions. Eur Urol. 2019;75(1):100–10.

    Article  Google Scholar 

  7. Sun M, Lughezzani G, Perrotte P, Karakiewicz PI. Treatment of metastatic renal cell carcinoma. Nat Rev Urol. 2010;7(6):327–38.

    Article  CAS  Google Scholar 

  8. Lopez JI, Cortes JM. Multisite tumor sampling: a new tumor selection method to enhance intratumor heterogeneity detection. Hum Pathol. 2017;64:1–6.

    Article  Google Scholar 

  9. Cancer Genome Atlas Research N. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;499(7456):43–9.

    Article  Google Scholar 

  10. Kapitsinou PP, Haase VH. The VHL tumor suppressor and HIF: insights from genetic studies in mice. Cell Death Differ. 2008;15(4):650–9.

    Article  CAS  Google Scholar 

  11. Dizman N, Philip EJ, Pal SK. Genomic profiling in renal cell carcinoma. Nat Rev Nephrol. 2020;16(8):435–51.

    Article  Google Scholar 

  12. Ni L, Yuan C, Zhang C, Xiang Y, Wu J, Wang X, Wu X. Co-expression network analysis identified LTF in association with metastasis risk and prognosis in clear cell renal cell carcinoma. Onco Targets Ther. 2020;13:6975–86.

    Article  CAS  Google Scholar 

  13. Sun Y, Zou J, Ouyang W, Chen K. Identification of as a potential core gene involved in the metastasis of clear cell renal cell carcinoma. Cancer Manage Res. 2020;12:5701–12.

    Article  CAS  Google Scholar 

  14. Ho TH, Serie DJ, Parasramka M, Cheville JC, Bot BM, Tan W, Wang L, Joseph RW, Hilton T, Leibovich BC, et al. Differential gene expression profiling of matched primary renal cell carcinoma and metastases reveals upregulation of extracellular matrix genes. Ann Oncol. 2017;28(3):604–10.

    Article  CAS  Google Scholar 

  15. Nam HY, Chandrashekar DS, Kundu A, Shelar S, Kho EY, Sonpavde G, Naik G, Ghatalia P, Livi CB, Varambally S, et al. Integrative epigenetic and gene expression analysis of renal tumor progression to metastasis. Mol Cancer Res. 2019;17(1):84–96.

    Article  CAS  Google Scholar 

  16. Wuttig D, Zastrow S, Fussel S, Toma MI, Meinhardt M, Kalman K, Junker K, Sanjmyatav J, Boll K, Hackermuller J, et al. CD31, EDNRB and TSPAN7 are promising prognostic markers in clear-cell renal cell carcinoma revealed by genome-wide expression analyses of primary tumors and metastases. Int J Cancer. 2012;131(5):E693-704.

    Article  CAS  Google Scholar 

  17. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.

    Article  CAS  Google Scholar 

  18. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–60.

    Article  CAS  Google Scholar 

  19. Zhang Z, Lin E, Zhuang H, Xie L, Feng X, Liu J, Yu Y. Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma. Cancer Cell Int. 2020;20:27.

    Article  Google Scholar 

  20. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50.

    Article  CAS  Google Scholar 

  21. Salgia NJ, Dara Y, Bergerot P, Salgia M, Pal SK. The changing landscape of management of metastatic renal cell carcinoma: current treatment options and future directions. Curr Treat Options Oncol. 2019;20(5):41.

    Article  Google Scholar 

  22. Lopez-Fernandez E, Lopez JI. The impact of tumor eco-evolution in renal cell carcinoma sampling. Cancers (Basel). 2018;10(12):485.

    Article  Google Scholar 

  23. Mouw JK, Ou G, Weaver VM. Extracellular matrix assembly: a multiscale deconstruction. Nat Rev Mol Cell Biol. 2014;15(12):771–85.

    Article  CAS  Google Scholar 

  24. Kai F, Drain AP, Weaver VM. The extracellular matrix modulates the metastatic journey. Dev Cell. 2019;49(3):332–46.

    Article  CAS  Google Scholar 

  25. Northey JJ, Przybyla L, Weaver VM. Tissue force programs cell fate and tumor aggression. Cancer Discov. 2017;7(11):1224–37.

    Article  CAS  Google Scholar 

  26. Lucotti S, Muschel RJ. Platelets and metastasis: new implications of an old interplay. Front Oncol. 2020;10:1350.

    Article  Google Scholar 

  27. Chapman K, Holmes M, Seckl J. 11beta-hydroxysteroid dehydrogenases: intracellular gate-keepers of tissue glucocorticoid action. Physiol Rev. 2013;93(3):1139–206.

    Article  CAS  Google Scholar 

  28. Agarwal AK, Rogerson FM, Mune T, White PC. Gene structure and chromosomal localization of the human HSD11K gene encoding the kidney (type 2) isozyme of 11 beta-hydroxysteroid dehydrogenase. Genomics. 1995;29(1):195–9.

    Article  CAS  Google Scholar 

  29. Zhang MZ, Xu J, Yao B, Yin H, Cai Q, Shrubsole MJ, Chen X, Kon V, Zheng W, Pozzi A, et al. Inhibition of 11beta-hydroxysteroid dehydrogenase type II selectively blocks the tumor COX-2 pathway and suppresses colon carcinogenesis in mice and humans. J Clin Invest. 2009;119(4):876–85.

    Article  CAS  Google Scholar 

  30. Qi L, Zhang Y, Song F, Ding Y. Chinese herbal medicine promote tissue differentiation in colorectal cancer by activating HSD11B2. Arch Biochem Biophys. 2020;695:108644.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Thanks to contributors to the GEO database and TCGA database for their platforms and datasets. And thanks to the researchers and study participants for their contributions.

Funding

This work was supported by a grant from The Intelligence Medicine Project of Chongqing Medical University, China (No. ZHYX2019015 to Longke Ran).

Author information

Authors and Affiliations

  1. Department of Bioinformatics, The Basic Medical School of Chongqing Medical University, Chongqing, 400016, China

    Shiqi Miao, Jiayi Lai & Longke Ran

  2. Laboratory of Forensic Medicine and Biomedical Informatics, Chongqing Medical University, Chongqing, 400016, China

    Shiqi Miao, Jiayi Lai & Longke Ran

  3. The Affiliated Luoyang Central Hospital of Zhengzhou University, No. 288, Zhongzhou Road, Luoyang, 471099, Henan, China

    Huirui Wang

  4. Molecular and Tumor Research Center, Chongqing Medical University, Chongqing, 400016, China

    Jing Song

  5. Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Qingyuan Liu

Authors
  1. Shiqi Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiayi Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huirui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Longke Ran
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. LR and HW designed the experimental flow. SM collected and downloaded the data. JS and QL analyzed the data. JL and SM wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Huirui Wang or Longke Ran.

Ethics declarations

Conflict of interest

The authors declared no conflict of interest.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. The study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (The ethical approval number: 2021-465). In addition, A preprint has previously been published in Research Square (link:https://www.researchsquare.com/article/rs-885592/v1). This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miao, S., Song, J., Liu, Q. et al. Integrated bioinformatics analysis to identify the key gene associated with metastatic clear cell renal cell carcinoma. Med Oncol 39, 128 (2022). https://doi.org/10.1007/s12032-022-01706-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12032-022-01706-y

Keywords

Search

Navigation