Skip to main content

Advertisement

Log in

Pan-Cancer Analysis of P3H1 and Experimental Validation in Renal Clear Cell Carcinoma

  • Original Article
  • Published:
Applied Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

Prolyl 3-hydroxylase 1 (P3H1) has been implicated in cancer development, but no pan-cancer analysis has been conducted on P3H1. In this study, for the first time, aspects associated with P3H1, such as the mRNA expression, any mutation, promoter methylation, and prognostic significance, the relationship between P3H1 and clinicopathological parameters, drug sensitivity, and immune cell infiltration were investigated by searching several databases including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), cBioPortal, and The Tumor Immune Evaluation Resource (TIMER2.0) using bioinformatics tools. The findings indicate significant differential expression of P3H1 in most tumors when compared to normal tissues, with a strong association with clinical prognosis. A pan-cancer Cox regression analysis revealed that high P3H1 expression is significantly associated with low overall survival in patients with brain lower grade glioma, kidney clear cell carcinoma, adrenocortical cancer, liver hepatocellular carcinoma, mesothelioma, sarcoma, uveal melanoma, bladder urothelial carcinoma, kidney papillary cell carcinoma, kidney chromophobe, thymoma, and thyroid carcinoma. A negative correlation was observed between P3H1 DNA methylation and its expression. P3H1 is significantly associated with infiltrating cells, immune-related genes, tumor mutation burden, microsatellite instability, and mismatch repair. Finally, A significant correlation was found between P3H1 expression and sensitivity to nine drugs. Thus, enhanced P3H1 expression is associated with poor prognosis in a variety of tumors, which may be due to its role in tumor immune regulation and tumor microenvironment. This pan-cancer analysis provides insight into the function of P3H1 in tumorigenesis of different cancers and provides a theoretical basis for further in-depth studies to follow.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability

The data sets used in this research are publicly available online.

References

  1. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., et al. (2021). Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 71, 209–249. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. Yang, M., Oh, I. Y., Mahanty, A., Jin, W. L., & Yoo, J. S. (2020). Immunotherapy for glioblastoma: current state, challenges, and future perspectives, Cancers (Basel), 12. https://doi.org/10.3390/cancers12092334

  3. Bisht, D., Arora, A., & Sachan, M. (2022). Role of dna de-methylation intermediate “5-hydroxymethylcytosine” in ovarian cancer management: A comprehensive review. Biomedicine & Pharmacotherapy, 155, 113674. https://doi.org/10.1016/j.biopha.2022.113674

    Article  CAS  Google Scholar 

  4. Liu, X., Chen, L., & Wang, T. (2022). Overcoming cisplatin resistance of human lung cancer by sinomenine through targeting the mir-200a-3p-gls axis, J Chemother, 1–10. https://doi.org/10.1080/1120009X.2022.2111490.

  5. Vranka, J. A., Pokidysheva, E., Hayashi, L., Zientek, K., Mizuno, K., et al. (2010). Prolyl 3-hydroxylase 1 null mice display abnormalities in fibrillar collagen-rich tissues such as tendons, skin, and bones. Journal of Biological Chemistry, 285, 17253–17262. https://doi.org/10.1074/jbc.M110.102228

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wu, J., Zhang, W., Xia, L., Feng, L., Shu, Z., et al. (2019). Characterization of ppib interaction in the p3h1 ternary complex and implications for its pathological mutations. Cellular and Molecular Life Sciences, 76, 3899–3914. https://doi.org/10.1007/s00018-019-03102-8

    Article  CAS  PubMed  Google Scholar 

  7. Hudson, D. M., Weis, M., Rai, J., Joeng, K. S., Dimori, M., et al. (2017). P3h3-null and sc65-null mice phenocopy the collagen lysine under-hydroxylation and cross-linking abnormality of ehlers-danlos syndrome type via. Journal of Biological Chemistry, 292, 3877–3887. https://doi.org/10.1074/jbc.M116.762245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cabral, W. A., Fratzl-Zelman, N., Weis, M., Perosky, J. E., Alimasa, A., et al. (2020). Substitution of murine type i collagen a1 3-hydroxylation site alters matrix structure but does not recapitulate osteogenesis imperfecta bone dysplasia. Matrix Biology, 90, 20–39. https://doi.org/10.1016/j.matbio.2020.02.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Tan, W., Ji, Y., Qian, Y., Lin, Y., Ye, R., et al. (2022). Mutational screening of skeletal genes in 14 chinese children with osteogenesis imperfecta using targeted sequencing. Journal of Immunology Research, 2022, 5068523. https://doi.org/10.1155/2022/5068523

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhytnik, L., Duy, B. H., Eekhoff, M., Wisse, L., & Pals, G., et al. (2022) Phenotypic variation in vietnamese osteogenesis imperfecta patients sharing a recessive p3h1 pathogenic variant, Genes (Basel), 13. https://doi.org/10.3390/genes13030407

  11. Nadyrshina, D., Zaripova, A., Tyurin, A., Minniakhmetov, I., Zakharova, E., & Khusainova, R. (2022). Osteogenesis imperfecta: search for mutations in patients from the republic of bashkortostan (russia), Genes (Basel), 13. https://doi.org/10.3390/genes13010124

  12. Tuysuz, B., Elkanova, L., Uludag, A. D., Gulec, C., Toksoy, G., et al. (2022). Osteogenesis imperfecta in 140 turkish families: Molecular spectrum and comparison of long-term clinical outcome of those with col1a1/a2 and biallelic variants. Bone, 155, 116293. https://doi.org/10.1016/j.bone.2021.116293

    Article  CAS  PubMed  Google Scholar 

  13. Scollo, P., Snead, M. P., Richards, A. J., Pollitt, R., & Devile, C. (2018). Bilateral giant retinal tears in osteogenesis imperfecta. BMC Medical Genetics, 19, 8. https://doi.org/10.1186/s12881-018-0521-0

    Article  PubMed  PubMed Central  Google Scholar 

  14. Pokidysheva, E., Tufa, S., Bresee, C., Brigande, J. V., & Bachinger, H. P. (2013). Prolyl 3-hydroxylase-1 null mice exhibit hearing impairment and abnormal morphology of the middle ear bone joints. Matrix Biology, 32, 39–44. https://doi.org/10.1016/j.matbio.2012.11.006

    Article  CAS  PubMed  Google Scholar 

  15. Chen, Z., Liu, G., Hossain, A., Danilova, I. G., Bolkov, M. A., et al. (2019). A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival. Hereditas, 156, 24. https://doi.org/10.1186/s41065-019-0100-1

    Article  PubMed  PubMed Central  Google Scholar 

  16. Li, Y., Chen, Y., Ma, Y., Nenkov, M., Haase, D., & Petersen, I. (2018). Collagen prolyl hydroxylase 3 has a tumor suppressive activity in human lung cancer. Experimental Cell Research, 363, 121–128. https://doi.org/10.1016/j.yexcr.2017.12.020

    Article  CAS  PubMed  Google Scholar 

  17. Xue, W., Sun, C., Yuan, H., Yang, X., Zhang, Q., et al. (2022). Establishment and analysis of an individualized emt-related gene signature for the prognosis of breast cancer in female patients. Disease Markers, 2022, 1289445. https://doi.org/10.1155/2022/1289445

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zhou, P., Liu, Z., Hu, H., Lu, Y., Xiao, J., et al. (2022). Comprehensive analysis of senescence characteristics defines a novel prognostic signature to guide personalized treatment for clear cell renal cell carcinoma. Frontiers in Immunology, 13, 901671. https://doi.org/10.3389/fimmu.2022.901671

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Consortium G. (2013). The genotype-tissue expression (gtex) project. Nature Genetics, 45, 580-585.https://doi.org/10.1038/ng.2653

  20. Nusinow, D. P., Szpyt, J., Ghandi, M., Rose, C. M., Mcdonald, E. R., et al. (2020). Quantitative proteomics of the cancer cell line encyclopedia. Cell, 180, 387–402. https://doi.org/10.1016/j.cell.2019.12.023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tomczak, K., Czerwinska, P., & Wiznerowicz, M. (2015). The cancer genome atlas (tcga): An immeasurable source of knowledge. Contemp Oncol (Pozn), 19, A68–A77. https://doi.org/10.5114/wo.2014.47136

    Article  PubMed  Google Scholar 

  22. Goldman, M. J., Craft, B., Hastie, M., Repecka, K., Mcdade, F., et al. (2020). Visualizing and interpreting cancer genomics data via the xena platform. Nature Biotechnology, 38, 675–678. https://doi.org/10.1038/s41587-020-0546-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., et al. (2012). The cbio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discovery, 2, 401–404. https://doi.org/10.1158/2159-8290.CD-12-0095

    Article  PubMed  Google Scholar 

  24. Zeng, D., Li, M., Zhou, R., Zhang, J., Sun, H., et al. (2019). Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures. Cancer Immunology Research, 7, 737–750. https://doi.org/10.1158/2326-6066.CIR-18-0436

    Article  CAS  PubMed  Google Scholar 

  25. Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., et al. (2020). Timer2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Research, 48, W509–W514. https://doi.org/10.1093/nar/gkaa407

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., et al. (2013). Genomics of drug sensitivity in cancer (gdsc): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research, 41, D955–D961. https://doi.org/10.1093/nar/gks1111

    Article  CAS  PubMed  Google Scholar 

  27. Lv, W., Shi, L., Pan, J., & Wang, S. (2022). Comprehensive prognostic and immunological analysis of cct2 in pan-cancer. Frontiers in Oncology, 12, 986990. https://doi.org/10.3389/fonc.2022.986990

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chen, F., Fan, Y., Cao, P., Liu, B., Hou, J., et al. (2021). Pan-cancer analysis of the prognostic and immunological role of hsf1: A potential target for survival and immunotherapy. Oxidative Medicine and Cellular Longevity, 2021, 5551036. https://doi.org/10.1155/2021/5551036

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Erbas, I. M., Ilgun, G. D., Manav, K. Z., Koc, A., Unuvar, T., et al. (2022). Clinical, genetic characteristics and treatment outcomes of children and adolescents with osteogenesis imperfecta: A two-center experience. Connective Tissue Research, 63, 349–358. https://doi.org/10.1080/03008207.2021.1932853

    Article  CAS  PubMed  Google Scholar 

  30. de Souza, L. T., Nunes, R. R., de Azevedo, M. O., & Maria, F. T. (2021). A new case of osteogenesis imperfecta type viii and retinal detachment. American Journal of Medical Genetics. Part A, 185, 238–241. https://doi.org/10.1002/ajmg.a.61934

    Article  CAS  PubMed  Google Scholar 

  31. Tang, C., Fang, M., Tan, G., Zhang, S., Yang, B., et al. (2022). Discovery of novel circulating immune complexes in lupus nephritis using immunoproteomics. Frontiers in Immunology, 13, 850015. https://doi.org/10.3389/fimmu.2022.850015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Gawel, D. R., Lee, E. J., Li, X., Lilja, S., Matussek, A., et al. (2019). An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer. Science and Reports, 9, 15575. https://doi.org/10.1038/s41598-019-51999-9

    Article  CAS  Google Scholar 

  33. Xiong, C., Wang, G., & Bai, D. (2020). A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes. Bioengineered, 11, 1034–1046. https://doi.org/10.1080/21655979.2020.1822715

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhang, Y., Li, C. Y., Pan, M., Li, J. Y., Ge, W., et al. (2021). Exploration of the key proteins of high-grade intraepithelial neoplasia to adenocarcinoma sequence using in-depth quantitative proteomics analysis. J Oncol, 2021, 5538756. https://doi.org/10.1155/2021/5538756

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. He, W., Lin, S., Guo, Y., Wu, Y., Zhang, L. L., et al. (2022). Targeted demethylation at znf154 promotor upregulates znf154 expression and inhibits the proliferation and migration of esophageal squamous carcinoma cells. Oncogene. https://doi.org/10.1038/s41388-022-02366-y

    Article  PubMed  PubMed Central  Google Scholar 

  36. Avram, E. G., Moatar, I. A., Miok, V., Baderca, F., Samoila, C., et al. (2022). Gene network analysis of the transcriptome impact of methylated micrornas on oral squamous cell carcinoma. Advances in Clinical and Experimental Medicine. https://doi.org/10.17219/acem/151911

    Article  PubMed  Google Scholar 

  37. Xu, Q., Lan, X., Lin, H., Xi, Q., & Wang, M., et al. (2022). Tumor microenvironment-regulating nanomedicine design to fight multi-drug resistant tumors, Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology, e1842. https://doi.org/10.1002/wnan.1842.

  38. Luo, J., Xie, Y., Zheng, Y., Wang, C., Qi, F., et al. (2020). Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the estimate algorithm. Cancer Medicine, 9, 4310–4323. https://doi.org/10.1002/cam4.2983

    Article  PubMed  PubMed Central  Google Scholar 

  39. Yoshihara, K., Shahmoradgoli, M., Martinez, E., Vegesna, R., Kim, H., et al. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications, 4, 2612. https://doi.org/10.1038/ncomms3612

    Article  CAS  PubMed  Google Scholar 

  40. Signorini, L., Delbue, S., Ferrante, P., & Bregni, M. (2016). Review on the immunotherapy strategies against metastatic colorectal carcinoma. Immunotherapy-Uk, 8, 1245–1261. https://doi.org/10.2217/imt-2016-0045

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge the TCGA, GTEx, CCLE, TIMER2.0, cBioPortal, and GDSC databases for free use.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization, Yongjie Li; Formal analysis, Yongjie Li and Ting Wang; Software analyses, Ting Wang; Visualization, Feng Jiang; Writing – original draft, Yongjie Li; Writing – review & editing, Yongjie Li.

Corresponding author

Correspondence to Yongjie Li.

Ethics declarations

Ethics Statement

All the experiments were conducted in accordance with the ethical guidelines of Shaoyang University.

Consent for Publication

Not applicable.

Conflict of Interest

The authors declare that there are no conflict of interests.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (TIF 9382 KB)

Supplementary file2 (TIFF 623 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Wang, T. & Jiang, F. Pan-Cancer Analysis of P3H1 and Experimental Validation in Renal Clear Cell Carcinoma. Appl Biochem Biotechnol (2024). https://doi.org/10.1007/s12010-023-04845-8

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12010-023-04845-8

Keywords

Navigation