Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Engineering artificial non-coding RNAs for targeted protein degradation

Nature Chemical Biology (2024)Cite this article

Subjects

Abstract

Targeted protein degradation has become a notable drug development strategy, but its application has been limited by the dependence on protein-based chimeras with restricted genetic manipulation capabilities. The use of long non-coding RNAs (lncRNAs) has emerged as a viable alternative, offering interactions with cellular proteins to modulate pathways and enhance degradation capabilities. Here we introduce a strategy employing artificial lncRNAs (alncRNAs) for precise targeted protein degradation. By integrating RNA aptamers and sequences from the lncRNA HOTAIR, our alncRNAs specifically target and facilitate the ubiquitination and degradation of oncogenic transcription factors and tumor-related proteins, such as c-MYC, NF-κB, ETS-1, KRAS and EGFR. These alncRNAs show potential in reducing malignant phenotypes in cells, both in vitro and in vivo, offering advantages in efficiency, adaptability and versatility. This research enhances knowledge of lncRNA-driven protein degradation and presents an effective method for targeted therapies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: alncRNA facilitates the ubiquitination and degradation of GFP.
Fig. 2: alncRNA promotes degradation of oncogenic TFs in BCa cell lines.
Fig. 3: Inhibition of cell proliferation induced by alncRNAs in BCa cell lines.
Fig. 4: Inhibition of cell migration induced by alncRNAs in BCa cell lines.
Fig. 5: Induction of apoptosis by alncRNAs in BCa cell lines.
Fig. 6: alncRNA inhibits tumor development and metastasis in vivo.

Similar content being viewed by others

Data availability

The data supporting the findings of this study are available within the paper and its Supplementary Information. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD052457. Additional information is available from the authors upon reasonable request. Source data are provided with this paper.

References

  1. Dang, C. V., Reddy, E. P., Shokat, K. M. & Soucek, L. Drugging the ‘undruggable’ cancer targets. Nat. Rev. Cancer 17, 502–508 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Bushweller, J. H. Targeting transcription factors in cancer—from undruggable to reality. Nat. Rev. Cancer 19, 611–624 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Duffy, M. J., O’Grady, S., Tang, M. & Crown, J. MYC as a target for cancer treatment. Cancer Treat. Rev. 94, 102154 (2021).

    Article  PubMed  CAS  Google Scholar 

  4. Huang, L., Guo, Z., Wang, F. & Fu, L. KRAS mutation: from undruggable to druggable in cancer. Signal Transduct. Target Ther. 6, 386 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Uprety, D. & Adjei, A. A. KRAS: from undruggable to a druggable cancer target. Cancer Treat. Rev. 89, 102070 (2020).

    Article  PubMed  CAS  Google Scholar 

  6. Neklesa, T. K., Winkler, J. D. & Crews, C. M. Targeted protein degradation by PROTACs. Pharmacol. Ther. 174, 138–144 (2017).

    Article  PubMed  CAS  Google Scholar 

  7. Ahn, G. et al. LYTACs that engage the asialoglycoprotein receptor for targeted protein degradation. Nat. Chem. Biol. 17, 937–946 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Cotton, A. D., Nguyen, D. P., Gramespacher, J. A., Seiple, I. B. & Wells, J. A. Development of antibody-based PROTACs for the degradation of the cell-surface immune checkpoint protein PD-L1. J. Am. Chem. Soc. 143, 593–598 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Li, X. et al. c-Myc-targeting PROTAC based on a TNA-DNA bivalent binder for combination therapy of triple-negative breast cancer. J. Am. Chem. Soc. 145, 9334–9342 (2023).

    Article  PubMed  CAS  Google Scholar 

  10. Shao, J. et al. Destruction of DNA-binding proteins by programmable oligonucleotide PROTAC (OʼPROTAC): effective targeting of LEF1 and ERG. Adv. Sci. 8, e2102555 (2021).

    Article  Google Scholar 

  11. Chen, M. et al. Inducible degradation of oncogenic nucleolin using an aptamer-based PROTAC. J. Med. Chem. 66, 1339–1348 (2023).

    Article  PubMed  CAS  Google Scholar 

  12. Shen, F. & Dassama, L. M. K. Opportunities and challenges of protein-based targeted protein degradation. Chem. Sci. 14, 8433–8447 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Xu, Y. et al. A heterobifunctional molecule recruits cereblon to an RNA scaffold and activates its PROTAC function. Cell Rep. Phys. Sci. 3, 101064 (2022).

    Article  CAS  Google Scholar 

  14. Batista, P. J. & Chang, H. Y. Long noncoding RNAs: cellular address codes in development and disease. Cell 152, 1298–1307 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Rinn, J. L. & Chang, H. Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).

    Article  PubMed  CAS  Google Scholar 

  16. Wang, K. C. & Chang, H. Y. Molecular mechanisms of long noncoding RNAs. Mol. Cell 43, 904–914 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Zhou, J. et al. Implications of protein ubiquitination modulated by lncRNAs in gastrointestinal cancers. Biochem. Pharmacol. 188, 114558 (2021).

    Article  PubMed  CAS  Google Scholar 

  18. Maruyama, R. & Suzuki, H. Long noncoding RNA involvement in cancer. BMB Rep. 45, 604–611 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Peng, W. X., Koirala, P. & Mo, Y. Y. LncRNA-mediated regulation of cell signaling in cancer. Oncogene 36, 5661–5667 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Farooqi, A. A. et al. Interplay of long non-coding RNAs and TGF/SMAD signaling in different cancers. Cell Mol. Biol. 64, 1–6 (2018).

    Article  PubMed  Google Scholar 

  21. Xie, H. et al. Synthetic artificial ‘long non-coding RNAs’ targeting oncogenic microRNAs and transcriptional factors inhibit malignant phenotypes of bladder cancer cells. Cancer Lett. 422, 94–106 (2018).

    Article  PubMed  CAS  Google Scholar 

  22. Wu, J. et al. Long non-coding RNA HNF1A-AS1 upregulates OTX1 to enhance angiogenesis in colon cancer via the binding of transcription factor PBX3. Exp. Cell Res. 393, 112025 (2020).

    Article  PubMed  CAS  Google Scholar 

  23. Wu, J. et al. Linc00152 promotes tumorigenesis by regulating DNMTs in triple-negative breast cancer. Biomed. Pharmacother. 97, 1275–1281 (2018).

    Article  PubMed  CAS  Google Scholar 

  24. Zhang, Q. et al. The characteristic landscape of lncRNAs classified by RBP–lncRNA interactions across 10 cancers. Mol. Biosyst. 13, 1142–1151 (2017).

    Article  PubMed  CAS  Google Scholar 

  25. Zhang, Z. et al. Long non-coding RNA CASC11 interacts with hnRNP-K and activates the WNT/β-catenin pathway to promote growth and metastasis in colorectal cancer. Cancer Lett. 376, 62–73 (2016).

    Article  PubMed  CAS  Google Scholar 

  26. He, R. Z., Luo, D. X. & Mo, Y. Y. Emerging roles of lncRNAs in the post-transcriptional regulation in cancer. Genes Dis. 6, 6–15 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Keefe, A. D., Pai, S. & Ellington, A. Aptamers as therapeutics. Nat. Rev. Drug Discov. 9, 537–550 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Ellington, A. D. & Szostak, J. W. In vitro selection of RNA molecules that bind specific ligands. Nature 346, 818–822 (1990).

    Article  PubMed  CAS  Google Scholar 

  29. Lee, H. K., Choi, Y. S., Park, Y. A. & Jeong, S. Modulation of oncogenic transcription and alternative splicing by β-catenin and an RNA aptamer in colon cancer cells. Cancer Res. 66, 10560–10566 (2006).

    Article  PubMed  CAS  Google Scholar 

  30. Culler, S. J., Hoff, K. G. & Smolke, C. D. Reprogramming cellular behavior with RNA controllers responsive to endogenous proteins. Science 330, 1251–1255 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Yoon, J. H. et al. Scaffold function of long non-coding RNA HOTAIR in protein ubiquitination. Nat. Commun. 4, 2939 (2013).

    Article  PubMed  Google Scholar 

  32. Xue, M. et al. HOTAIR induces the ubiquitination of Runx3 by interacting with Mex3b and enhances the invasion of gastric cancer cells. Gastric Cancer 21, 756–764 (2018).

    Article  PubMed  CAS  Google Scholar 

  33. Mondragón, E. & Maher, L. J. III. Anti-transcription factor RNA aptamers as potential therapeutics. Nucleic Acid Ther. 26, 29–43 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Weigand, J. E., Wittmann, A. & Suess, B. RNA-based networks: using RNA aptamers and ribozymes as synthetic genetic devices. Methods Mol. Biol. 813, 157–168 (2012).

    Article  PubMed  CAS  Google Scholar 

  35. Guo, Q. et al. Dissecting the in vivo assembly of the 30S ribosomal subunit reveals the role of RimM and general features of the assembly process. Nucleic Acids Res. 41, 2609–2620 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Liu, M. et al. PRMT5-dependent transcriptional repression of c-Myc target genes promotes gastric cancer progression. Theranostics 10, 4437–4452 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Sanjari, M. et al. Enhanced expression of cyclin D1 and C-myc, a prognostic factor and possible mechanism for recurrence of papillary thyroid carcinoma. Sci. Rep. 10, 5100 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Shostak, K. & Chariot, A. EGFR and NF-κB: partners in cancer. Trends Mol. Med. 21, 385–393 (2015).

    Article  PubMed  CAS  Google Scholar 

  39. Boakye, Y. D. et al. Regulation of Nrf2 and NF-κB activities may contribute to the anti-inflammatory mechanism of xylopic acid. Inflammopharmacology 30, 1835–1841 (2022).

    Article  PubMed  CAS  Google Scholar 

  40. Stephen, A. G., Esposito, D., Bagni, R. K. & McCormick, F. Dragging Ras back in the ring. Cancer Cell 25, 272–281 (2014).

    Article  PubMed  CAS  Google Scholar 

  41. Jeong, S., Han, S. R., Lee, Y. J., Kim, J. H. & Lee, S. W. Identification of RNA aptamer specific to mutant KRAS protein. Oligonucleotides 20, 155–161 (2010).

    Article  PubMed  CAS  Google Scholar 

  42. Liu, L. et al. Inhibiting cell migration and cell invasion by silencing the transcription factor ETS-1 in human bladder cancer. Oncotarget 7, 25125–25134 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Esposito, C. L. et al. A neutralizing RNA aptamer against EGFR causes selective apoptotic cell death. PLoS ONE 6, e24071 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Salerno, D. et al. Hepatitis B protein HBx binds the DLEU2 lncRNA to sustain cccDNA and host cancer-related gene transcription. Gut 69, 2016–2024 (2020).

    Article  PubMed  CAS  Google Scholar 

  45. Wurster, S. E., Bida, J. P., Her, Y. F. & Maher, L. J. III. Characterization of anti-NF-κB RNA aptamer-binding specificity in vitro and in the yeast three-hybrid system. Nucleic Acids Res. 37, 6214–6224 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Jian, Y. et al. RNA aptamers interfering with nucleophosmin oligomerization induce apoptosis of cancer cells. Oncogene 28, 4201–4211 (2009).

    Article  PubMed  CAS  Google Scholar 

  47. Shi, X., Sun, M., Liu, H., Yao, Y. & Song, Y. Long non-coding RNAs: a new frontier in the study of human diseases. Cancer Lett. 339, 159–166 (2013).

    Article  PubMed  CAS  Google Scholar 

  48. Mercer, T. R., Munro, T. & Mattick, J. S. The potential of long noncoding RNA therapies. Trends Pharmacol. Sci. 43, 269–280 (2022).

    Article  PubMed  CAS  Google Scholar 

  49. Zeller, K. I. et al. Global mapping of c-Myc binding sites and target gene networks in human B cells. Proc. Natl Acad. Sci. USA 103, 17834–17839 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Mi, J. et al. RNA aptamer-targeted inhibition of NF-κB suppresses non-small cell lung cancer resistance to doxorubicin. Mol. Ther. 16, 66–73 (2008).

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the National Key R&D Program of China (2021YFA0911600) to Y.L.; the National Natural Science Foundation of China (82273135) to L.Y.; the National Natural Science Foundation of China (82303113) to A.L.; the Shenzhen Science and Technology Program (RCJC20221008092723011 and JCYJ20220818102001002) to Y.L.; the Beijing Municipal Science & Technology Commission (Z221100007422073) to L.Y.; the Guangdong Basic and Applied Basic Research Foundation (2023A1515111041) to C.C.; and the research fund of the Synthetic Biology Research Center of Shenzhen University to Y.L.

Author information

Author notes

  1. These authors contributed equally: Congcong Cao, Aolin Li.

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Systems Biology and Synthetic Biology for Urogenital Tumors, Shenzhen Institute of Translational Medicine, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Synthetic Biology Research Center, Health Science Center, Shenzhen University, Shenzhen, China

    Congcong Cao & Yuchen Liu

  2. Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China

    Aolin Li

  3. Department of Urology, Peking University First Hospital, Beijing, China

    Chaojie Xu & Lin Yao

  4. Department of Urology, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China

    Baorui Wu

Authors
  1. Congcong Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aolin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaojie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Baorui Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuchen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.C. performed data analysis and contributed to paper preparation. A.L. assisted with the design of cell experiments and provided technical support. C.X. conducted statistical analysis and helped with the interpretation of results. B.W. was involved in the design and execution of mouse experiments. L.Y. was responsible for securing funding and overseeing the process, while Y.L. provided the conceptual framework, designed the experiments and also secured funding.

Corresponding authors

Correspondence to Lin Yao or Yuchen Liu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks Da Jia, John Rossi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 The protein quantification results from main figures.

Figures A-H correspond to the quantitative statistical results of Figs. 1a, e, g, h, i, 2c, d in the main text, respectively. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test.

Extended Data Fig. 2 The specificity of alncRNA.

(A) RIP analysis of the interaction of alncRNA with a panel of RBPs in cell lysates. Antibodies recognizing the RBPs shown were used for IP in each case; control IP reactions were carried out using a corresponding IgG. AlncRNA levels were measured by RT-qPCR and normalized to the levels of GAPDH mRNA levels in the same IP samples measured by RT-qPCR analysis. Data were quantified as enrichment of alncRNA in the RBP IP relative to the IgG IP. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (B-C) The protein levels of RBPs were analyzed in alncRNA-expressing cells by WB. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (D) Proteomics analysis ensures the specificity of alncRNA. The data were analyzed using a two-tailed unpaired Student’s t-test. Adjustments were made for multiple comparisons using the Bonferroni correction method. (E-F) RIP assays show that alncRNA is pulled down by Dzip3 and GFP antibody in cells. Immunoprecipitation with control IgG served as the negative control. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (G) RNA pull-down assays showed that Dzip3 and GFP was pulled down by alncRNA. Antisense of alncRNA was used as negative control. (H-I) The effect of reporter gene activation was determined by dual-luciferase reporter assay. CRISPR plasmids were transfected into cells stably expressing a dual-luciferase reporter vector. Relative luciferase activities were determined as the ratios between Rluc and Fluc values. Data represent mean ± s.d. from four independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (J-K) Fold change in abundance of whole-cell proteins detected using quantitative proteomics analysis after c-Myc (J) and NF-κB (K) degradation. The data were analyzed using a two-tailed unpaired Student’s t-test. Adjustments were made for multiple comparisons using the Bonferroni correction method.

Source data

Extended Data Fig. 3 The dependence of the protein degradation function of alncRNA on Dzip3.

(A) Forty-eight hours after transfecting the shRNAs shown and alncRNA, the levels of GFP, Dzip3 and loading control GAPDH were assessed by WB analysis. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (B) Forty-eight hours after overexpressing plasmids Dzip3 mutant or Dzip3 WT in cells, the levels of GFP, Dzip3 (WT and mutant) and loading control GAPDH were assessed by WB analysis. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (C) Forty-eight hours after transfecting plasmids GFP WT or GFP mutant, the levels of GFP (WT and mutant) and loading control GAPDH were assessed by WB analysis. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (D) Following transfection of cells with plasmids GFP WT or GFP mutant, RIP analysis was performed to quantify the interaction of WT GFP and mutant GFP with alncRNA; binding was normalized to GAPDH mRNA levels in the IP samples. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (E) The half-lives (t1/2) of alncRNA and GAPDH mRNA were quantified by measuring the rate of decline in transcript levels by RT-qPCR. Data represent the means and s.d. from three independent experiments.

Source data

Extended Data Fig. 4 The target proteins degraded by alncRNA in different tumor cell lines.

(A-B) The alncRNA facilitates the degradation of c-MYC (A) and NF-κB (B) in different tumor cell lines. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (C-D) The target genes of c-MYC (C) and NF-κB (D) were downregulated by alncRNA. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (E) The alncRNA promotes degradation of KRAS in pancreatic cancer cell lines. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test.

Source data

Extended Data Fig. 5 The gradual decrease in the protein levels of c-MYC and NF-κB after treated with alncRNA.

(A-B) The results illustrate the time-dependent degradation of c-MYC (A) and NF-κB (B) proteins mediated by alncRNA. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (C-D) Flow cytometry analysis revealed the apoptosis rate of bladder cancer cells transfected with alncRNA targeting c-MYC (C) and NF-κB (D) protein degradation at 24 h. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test.

Source data

Extended Data Fig. 6 The tumor-suppressive effects of AAV-Dzip3-c-Myc after Dzip3 knockdown.

(A-B) The quantification results of Edu positive cells in Fig. 3c, d (A) and Fig. 3g, h (B). Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (C) Measurement of 3 pairs of metastatic model’s bioluminescence imaging. (D) The survival curve of mice showed that tail vein injection of AAV-Dzip3-c-MYC did not significantly shorten the survival time of the mice. (E) ELISA results showing the levels of IL-6, TNF-α and IL-1β in the serum of BALB/c nude mice. (F-G) Forty-eight hours after transfecting the shRNAs shown and alncRNA, the levels of c-MYC, Dzip3 and loading control GAPDH were assessed by WB analysis. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (H-I) Tumor growth assays demonstrate that when Dzip3 is knocked down in cells, AAV-Dzip3-c-Myc does not exhibit a significant tumor-suppressive effect compared to the control Dzip3-NC. The number of mice for each group was 6 (n = 6 mice / group). Data represent mean ± s.d. The P value was determined by a two-tailed unpaired Student’s t test.

Source data

Extended Data Fig. 7 The alncRNA promotes degradation of ETS-1 in bladder cancer cell lines.

(A) The interaction between ETS-1 and Dzip3 was tested by immunoprecipitation. (B-C) The protein levels of ETS-1 were analyzed in alncRNA-expressing BCa cells by WB. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (D-E) After alncRNA degraded ETS-1 protein expression, wound healing assays were utilized to assess the migratory abilities of two bladder cancer cells. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (F) After alncRNA degraded ETS-1 protein expression, transwell assays were utilized to assess the cell motility of two bladder cancer cells. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (G) Measurement of a metastatic model’s bioluminescence imaging. The signal intensities for luminescence are displayed. The number of metastatic nodules formed in the lungs of mice for each group was 6 (n = 6 mice / group). Data represent mean ± s.d. The P value was determined by a two-tailed unpaired Student’s t test.

Source data

Extended Data Fig. 8 The alncRNA promotes degradation of EGFR in bladder cancer cell lines.

(A-B) The protein levels of EGFR were analyzed in alncRNA-expressing BCa cells by WB. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (C) The mRNA levels of EGFR were analyzed in alncRNA-expressing BCa cells by RT-qPCR. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (D-E) The CCK-8 assay demonstrated the effect of alncRNA-degraded EGFR protein expression on the proliferation of bladder cancer cells (T24 and 5637). Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-way ANOVA. (F-H) After alncRNA degraded EGFR protein expression, wound healing assays were utilized to assess the migratory abilities of two bladder cancer cells. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (I) The caspase-3/ELISA assay demonstrates the impact of alncRNA targeting EGFR protein degradation on cell apoptosis (T24 and 5637). Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test. (J) The target genes of EGFR were downregulated by alncRNA. Data represent mean ± s.d. from three independent experiments. The P values were determined by a two-tailed unpaired Student’s t test.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, Figs. 1–3 and Note 1.

Reporting Summary

Source data

Source Data Fig. 1

Unprocessed WBs and/or gels.

Source Data Fig. 2

Unprocessed WBs and/or gels.

Source Data Extended Data Fig. 2

Unprocessed WBs and/or gels.

Source Data Extended Data Fig. 3

Unprocessed WBs and/or gels.

Source Data Extended Data Fig. 4

Unprocessed WBs and/or gels.

Source Data Extended Data Fig. 5

Unprocessed WBs and/or gels.

Source Data Extended Data Fig. 6

Unprocessed WBs and/or gels.

Source Data Extended Data Fig. 7

Unprocessed WBs and/or gels.

Source Data Extended Data Fig. 8

Unprocessed WBs and/or gels.

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

Cao, C., Li, A., Xu, C. et al. Engineering artificial non-coding RNAs for targeted protein degradation. Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01719-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41589-024-01719-w

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing