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Multidimensional Transcriptomics Unveils RNF34 as a Prognostic Biomarker and Potential Indicator of Chemotherapy Sensitivity in Wilms’ Tumour

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Abstract

Nephroblastoma, colloquially known as Wilms’ tumour (WT), is the predominant malignant renal neoplasm arising in the paediatric population. Modern therapeutic approaches for WT incorporate a synergistic combination of surgical intervention, radiotherapy, and chemotherapy, which substantially ameliorate the overall patient survival rate. Despite this, the optimal sequence of chemotherapy and surgical intervention remains a matter of contention, with each strategy presenting its own strengths and weaknesses that could influence clinical decision-making. To make some headway on this clinical dilemma, we deployed a multidimensional transcriptomics integration approach by analysing bulk RNA sequencing data with 136 samples, as well as single-nucleus RNA sequencing (snRNA-seq) and paired spatial transcriptome sequencing (stRNA) data from 32 WT specimens. Our findings identified a distinct elevation of RNF34 expression within WT samples, which correlated with unfavourable prognostic outcomes. Leveraging the Genomics of Drug Sensitivity in Cancer (GDSC), we simultaneously revealed that patients with high expression of RNF34 have higher sensitivity to commonly used chemotherapy drugs for WT. Furthermore, our analysis of snRNA and stRNA data unveiled a reduced proportion of RNF34 expression in neoplastic cells after chemotherapy. Moreover, stRNA data delineated a significant association between a higher proportion of RNF34 expression in cancer cells and adverse features such as anaplastic histology and tumour recurrence. Intriguingly, we also observed a close association between elevated RNF34 expression and a characteristic exhausted tumour immune microenvironment. Collectively, our findings underscore the pivotal role of RNF34 in the prognostic prediction potential and treatment sensitivity of WT. This comprehensive analysis can potentially inform and refine clinical decision-making for WT patients and guide future studies towards the development of optimized, rational therapeutic strategies.

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Data Availability

The bulk RNA-seq data and clinical data of Wilms’ tumour (WT) tissue samples were sourced from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The GSE2712 cohort and GSE66405 cohort were accessed from the Gene Expression Omnibus (GEO) database using the GEOquery R package. In addition, the pairs of snRNA-seq data and stRNA data were obtained from the Single-cell Pediatric Cancer Atlas Portal (https://scpca.alexslemonade.org/).

References

  1. Das, A., Tanigawa, S., Karner, C. M., Xin, M., Lum, L., Chen, C., et al. (2013). Stromal-epithelial crosstalk regulates kidney progenitor cell differentiation. Nature Cell Biology, 15(9), 1035–1044.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Fetting, J. L., Guay, J. A., Karolak, M. J., Iozzo, R. V., Adams, D. C., Maridas, D. E., et al. (2014). FOXD1 promotes nephron progenitor differentiation by repressing decorin in the embryonic kidney. Development, 141(1), 17–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Perotti, D., Hohenstein, P., Bongarzone, I., Maschietto, M., Weeks, M., Radice, P., et al. (2013). Is Wilms tumor a candidate neoplasia for treatment with WNT/beta-catenin pathway modulators? A report from the renal tumors biology-driven drug development workshop. Molecular Cancer Therapeutics, 12(12), 2619–2627.

    Article  CAS  PubMed  Google Scholar 

  4. Nakata, K., Colombet, M., Stiller, C. A., Pritchard-Jones, K., Steliarova-Foucher, E., Contributors I. (2020). Incidence of childhood renal tumours: An international population-based study. International Journal of Cancer, 147(12), 3313–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. D’Angio, G. J., Evans, A., Breslow, N., Beckwith, B., Bishop, H., Farewell, V., et al. (1981). The treatment of Wilms’ tumor: Results of the Second National Wilms’ Tumor Study. Cancer, 47(9), 2302–2311.

    Article  CAS  PubMed  Google Scholar 

  6. Nelson, M. V., van den Heuvel-Eibrink, M. M., Graf, N., & Dome, J. S. (2021). New approaches to risk stratification for Wilms tumor. Current Opinion in Pediatrics, 33(1), 40–48.

    Article  CAS  PubMed  Google Scholar 

  7. de la Monneraye, Y., Michon, J., Pacquement, H., Aerts, I., Orbach, D., Doz, F., et al. (2019). Indications and results of diagnostic biopsy in pediatric renal tumors: A retrospective analysis of 317 patients with critical review of SIOP guidelines. Pediatric Blood & Cancer, 66(6), e27641.

    Article  Google Scholar 

  8. Zhang, R., Zhao, J., Song, Y., Wang, X., Wang, L., Xu, J., et al. (2014). The E3 ligase RNF34 is a novel negative regulator of the NOD1 pathway. Cellular Physiology and Biochemistry, 33(6), 1954–1962.

    Article  CAS  PubMed  Google Scholar 

  9. Konishi, T., Sasaki, S., Watanabe, T., Kitayama, J., & Nagawa, H. (2005). Overexpression of hRFI (human ring finger homologous to inhibitor of apoptosis protein type) inhibits death receptor-mediated apoptosis in colorectal cancer cells. Molecular Cancer Therapeutics, 4(5), 743–750.

    Article  CAS  PubMed  Google Scholar 

  10. McDonald, E. R., 3rd., & El-Deiry, W. S. (2004). Suppression of caspase-8- and -10-associated RING proteins results in sensitization to death ligands and inhibition of tumor cell growth. Proceedings of the National Academy of Sciences of the United States of America, 101(16), 6170–6175.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Thompson, C. B. (1995). Apoptosis in the pathogenesis and treatment of disease. Science, 267(5203), 1456–1462.

    Article  CAS  PubMed  Google Scholar 

  12. Konishi, T., Sasaki, S., Watanabe, T., Kitayama, J., & Nagawa, H. (2006). Overexpression of hRFI inhibits 5-fluorouracil-induced apoptosis in colorectal cancer cells via activation of NF-kappaB and upregulation of BCL-2 and BCL-XL. Oncogene, 25(22), 3160–3169.

    Article  CAS  PubMed  Google Scholar 

  13. Sasaki, S., Kitayama, J., Watanabe, T., Konishi, T., & Nagawa, H. (2004). Diffuse expression of hRFI is correlated with blood vessel invasion in gastric carcinoma. Japanese Journal of Clinical Oncology, 34(10), 584–587.

    Article  PubMed  Google Scholar 

  14. Sasaki, S., Watanabe, T., Konishi, T., Kitayama, J., & Nagawa, H. (2004). Effects of expression of hRFI on adenoma formation and tumor progression in colorectal adenoma-carcinoma sequence. Journal of Experimental & Clinical Cancer Research, 23(3), 507–512.

    CAS  Google Scholar 

  15. Cutcliffe, C., Kersey, D., Huang, C. C., Zeng, Y., Walterhouse, D., Perlman, E. J., et al. (2005). Clear cell sarcoma of the kidney: Up-regulation of neural markers with activation of the sonic hedgehog and Akt pathways. Clinical Cancer Research, 11(22), 7986–7994.

    Article  CAS  PubMed  Google Scholar 

  16. Ludwig, N., Werner, T. V., Backes, C., Trampert, P., Gessler, M., Keller, A., et al. (2016). Combining miRNA and mRNA expression profiles in wilms tumor subtypes. International Journal of Molecular Sciences, 17(4), 475.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Davis, S., & Meltzer, P. S. (2007). GEOquery: A bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 23(14), 1846–1847.

    Article  PubMed  Google Scholar 

  18. Yoshihara, K., Shahmoradgoli, M., Martinez, E., Vegesna, R., Kim, H., Torres-Garcia, W., et al. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications, 4, 2612.

    Article  PubMed  Google Scholar 

  19. Hänzelmann, S., Castelo, R., & Guinney, J. (2013). GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 14, 7.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Moody, S. E., Dunn, I. F., et al. (2009). Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 462(7269), 108–112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova, M., Rieder, D., et al. (2017). Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Reports, 18(1), 248–262.

    Article  CAS  PubMed  Google Scholar 

  22. Yu, G., Wang, L. G., Han, Y., & He, Q. Y. (2012). clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology, 16(5), 284–287.

    Article  CAS  PubMed  Google Scholar 

  23. Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., et al. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research, 41, D955-61.

    Article  CAS  PubMed  Google Scholar 

  24. Geeleher, P., Cox, N. J., & Huang, R. S. (2014). Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biology, 15(3), R47.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yuan, R., Chen, S., & Wang, Y. (2020). Computational prediction of drug responses in cancer cell lines from cancer omics and detection of drug effectiveness related methylation sites. Frontiers in Genetics, 11, 917.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yang, C., Chen, J., Li, Y., Huang, X., Liu, Z., Wang, J., et al. (2021). Exploring subclass-specific therapeutic agents for hepatocellular carcinoma by informatics-guided drug screen. Briefings in Bioinformatics, 22(4), bbaa295.

    Article  PubMed  Google Scholar 

  27. Dome, J. S., Graf, N., Geller, J. I., Fernandez, C. V., Mullen, E. A., Spreafico, F., et al. (2015). Advances in Wilms Tumor Treatment and Biology: Progress through international collaboration. Journal of Clinical Oncology, 33(27), 2999–3007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Young, M. D., Mitchell, T. J., Vieira Braga, F. A., Tran, M. G. B., Stewart, B. J., Ferdinand, J. R., et al. (2018). Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science, 361(6402), 594–599.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wu, H., Kirita, Y., Donnelly, E. L., & Humphreys, B. D. (2019). Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: Rare cell types and novel cell states revealed in fibrosis. Journal of the American Society of Nephrology, 30(1), 23–32.

    Article  CAS  PubMed  Google Scholar 

  30. Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., et al. (2020). A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nature Medicine, 26(5), 792–802.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Guo, Y., Wang, W., Ye, K., He, L., Ge, Q., Huang, Y., et al. (2023). Single-nucleus RNA-seq: Open the era of great navigation for FFPE tissue. International Journal of Molecular Sciences, 24(18), 13744.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sasaki, S., Nakamura, T., Arakawa, H., Mori, M., Watanabe, T., Nagawa, H., et al. (2002). Isolation and characterization of a novel gene, hRFI, preferentially expressed in esophageal cancer. Oncogene, 21(32), 5024–5030.

    Article  CAS  PubMed  Google Scholar 

  33. Binnewies, M., Roberts, E. W., Kersten, K., Chan, V., Fearon, D. F., Merad, M., et al. (2018). Understanding the tumor immune microenvironment (TIME) for effective therapy. Nature Medicine, 24(5), 541–550.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Guangxi Natural Science Foundation (No. 2022GXNSFAA035641 and No. 2023GXNSFAA026134), the Scientific Research Project of Guangxi Provincial Health and Family Planning Commission (No. Z20200317) “Medical Excellence Award” Funded by the Creative Research Development Grant from the First Affiliated Hospital of Guangxi Medical University.

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  1. Jie Zheng and Fengling Liu have contributed equally to this work.

Authors and Affiliations

  1. Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China

    Jie Zheng

  2. Department of Pediatric Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China

    Jinwei Tuo, Siyu Chen, Jinxia Su, Xiuyi Ou, Min Ding, Haoran Chen, Bo Shi, Yong Li, Congjun Wang & Cheng Su

  3. Department of Pediatrics, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China

    Xun Chen

  4. Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China

    Jie Zheng, Fengling Liu & Cheng Su

  5. Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China

    Fengling Liu

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Contributions

JZ, FL, CS, CW and XC contributed to the study design; FL, JT and SC performed immunohistochemistry; JT, SC, JS, XO, MD and HC collected samples and patient information; JZ, FL contributed to the bioinformatics analyses; CS, CW and XC directed the study, obtained funding, and revised the manuscript. JZ and FL wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xun Chen, Congjun Wang or Cheng Su.

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12033_2023_1008_MOESM1_ESM.tif

Supplementary file1 Supplementary Figure 1. Expression comparison of RNF34 between relapse situation based on snRNA data. (A) Featureplot displaying the expression of RNF34 with no relapse (left) and relapse (right). (B) Expression ratio of RNF34 in different cell-types between relapse-no and relapse-yes patients. Cells were defined as RNF34-high group if they expressed RNF34 and as RNF34-low group if they did not express RNF34. (TIF 4747 KB)

12033_2023_1008_MOESM2_ESM.tif

Supplementary file2 Supplementary Figure 2. Expression comparison of RNF34 between anaplastic and favourable patients based on snRNA data. (A) Featureplot displaying the expression of RNF34 between anaplastic (left) and favourable patients (right). (B) Expression ratio of RNF34 in different cell-types between anaplastic and favourable patients. Cells were defined as RNF34-high group if they expressed RNF34 and as RNF34-low group if they did not express RNF34. (TIF 4787 KB)

12033_2023_1008_MOESM3_ESM.tif

Supplementary file3 Supplementary Figure 3. Expression characteristics of RNF34 based on stRNA data. (A) Featureplot showing the expression of RNF34 in spatial locations. The pie plot reveals the number of RNF34-positive cells in cancer cells. Tumour cells were defined as RNF34-high group if they expressed RNF34 and as RNF34-low group if they did not express RNF34 (TIF 281719 KB)

Supplementary file4 (XLSX 13 KB)

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Zheng, J., Liu, F., Tuo, J. et al. Multidimensional Transcriptomics Unveils RNF34 as a Prognostic Biomarker and Potential Indicator of Chemotherapy Sensitivity in Wilms’ Tumour. Mol Biotechnol 66, 1132–1143 (2024). https://doi.org/10.1007/s12033-023-01008-2

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