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Systemic analysis of the prognostic significance and interaction network of miR-26b-3p in cholangiocarcinoma

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Abstract

MicroRNAs (miRNAs) reportedly play significant roles in the progression of various cancers and hold huge potential as both diagnostic tools and therapeutic targets. Given the ongoing uncertainty surrounding the precise functions of several miRNAs in cholangiocarcinoma (CCA), this research undertakes a comprehensive analysis of CCA data sourced from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The present study identified a novel miRNA, specifically miR-26b-3p, which exhibited prognostic value for individuals with CCA. Notably, miR-26b-3p was upregulated within CCA samples, with an inverse correlation established with patient prognosis (Hazard Ratio = 8.19, p = 0.018). Through a combination of functional enrichment analysis, analysis of the LncRNA-miR-26b-3p-mRNA interaction network, and validation by qRT PCR and western blotting, this study uncovered the potential of miR-26b-3p in potentiating the malignant progression of CCA via regulation of essential genes (including PSMD14, XAB2, SLC4A4) implicated in processes such as endoplasmic reticulum (ER) stress and responses to misfolded proteins. Our findings introduce novel and valuable insights that position miR-26b-3p-associated genes as promising biomarkers for the diagnosis and treatment of CCA.

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Data Availability

The data generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Razumilava, N., & Gores, G. J. (2014). Cholangiocarcinoma. Lancet, 383(9935), 2168–2179.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Xue, R., Chen, L., Zhang, C., Fujita, M., Li, R., Yan, S. M., et al. (2019). Genomic and transcriptomic profiling of combined hepatocellular and intrahepatic cholangiocarcinoma reveals distinct molecular subtypes. Cancer Cell, 35(6), 932–47.8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Doherty, B., Nambudiri, V. E., & Palmer, W. C. (2017). Update on the diagnosis and treatment of cholangiocarcinoma. Current Gastroenterology Reports, 19(1), 2.

    Article  PubMed  Google Scholar 

  4. Tang, T. Y., Huang, X., Zhang, G., Lu, M. H., & Liang, T. B. (2022). mRNA vaccine development for cholangiocarcinoma: A precise pipeline. Military Medical Research, 9(1), 40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cillo, U., Fondevila, C., Donadon, M., Gringeri, E., Mocchegiani, F., Schlitt, H. J., et al. (2019). Surgery for cholangiocarcinoma. Liver International, 39(Suppl 1), 143–155.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bartel, D. P. (2004). MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell, 116(2), 281–297.

    Article  CAS  PubMed  Google Scholar 

  7. Tafrihi, M., & Hasheminasab, E. (2019). MiRNAs: Biology, Biogenesis, their Web-based Tools, and Databases. MicroRNA, 8(1), 4–27.

    Article  CAS  PubMed  Google Scholar 

  8. Tutar, L., Ozgur, A., & Tutar, Y. (2018). Involvement of miRNAs and Pseudogenes in Cancer. Methods in Molecular Biology, 1699, 45–66.

    Article  CAS  PubMed  Google Scholar 

  9. Hussen, B. M., Rasul, M. F., Abdullah, S. R., Hidayat, H. J., Faraj, G. S. H., Ali, F. A., et al. (2023). Targeting miRNA by CRISPR/Cas in cancer: Advantages and challenges. Military Medical Research, 10(1), 32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Li, Z., Shen, J., Chan, M. T., & Wu, W. K. (2017). The role of microRNAs in intrahepatic cholangiocarcinoma. Journal of Cellular and Molecular Medicine, 21(1), 177–184.

    Article  CAS  PubMed  Google Scholar 

  11. Cao, J., Sun, L., Li, J., Zhou, C., Cheng, L., Chen, K., et al. (2018). A novel three-miRNA signature predicts survival in cholangiocarcinoma based on RNA-Seq data. Oncology Reports, 40(3), 1422–1434.

    CAS  PubMed  Google Scholar 

  12. Yao, Y., Jiao, D., Liu, Z., Chen, J., Zhou, X., Li, Z., et al. (2020). Novel miRNA Predicts Survival and Prognosis of Cholangiocarcinoma Based on RNA-seq Data and In Vitro Experiments. BioMed Research International, 2020, 5976127.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wu, H. Y., Xia, S., Liu, A. G., Wei, M. D., Chen, Z. B., Li, Y. X., et al. (2019). Upregulation of miR-132-3p in cholangiocarcinoma tissues: A study based on RT-qPCR, The Cancer Genome Atlas miRNA sequencing, Gene Expression Omnibus microarray data and bioinformatics analyses. Molecular Medicine Reports, 20(6), 5002–5020.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhao, Y., Chen, J., Hao, Y., Wang, B., Wang, Y., Liu, Q., et al. (2022). Predicting the recurrence of chronic rhinosinusitis with nasal polyps using nasal microbiota. Allergy, 77(2), 540–549.

    Article  CAS  PubMed  Google Scholar 

  15. Liang, J. Y., Wang, D. S., Lin, H. C., Chen, X. X., Yang, H., Zheng, Y., et al. (2020). A novel ferroptosis-related gene signature for overall survival prediction in patients with Hepatocellular Carcinoma. International Journal of Biological Sciences, 16(13), 2430–2441.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chen, D., Liu, J., Zang, L., Xiao, T., Zhang, X., Li, Z., et al. (2022). Integrated machine learning and bioinformatic analyses constructed a novel stemness-related classifier to predict prognosis and immunotherapy responses for hepatocellular carcinoma patients. International Journal of Biological Sciences, 18(1), 360–373.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ranstam, J., & Cook, J. A. (2018). LASSO regression. British Journal of Surgery., 105, 1338–1348.

    Article  Google Scholar 

  18. Muthukrishnan R, Rohini R. LASSO: A feature selection technique in predictive modeling for machine learning. 2016 Ieee International Conference on Advances in Computer Applications (Icaca). https://academic.oup.com/bjs/article/105/10/1348/6122951. https://doi.org/10.1002/bjs.10895

  19. Kim, S. M., Kim, Y., Jeong, K., Jeong, H., & Kim, J. (2018). Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography. Ultrasonography, 37(1), 36–42.

    Article  PubMed  Google Scholar 

  20. Vasaikar, S. V., Straub, P., Wang, J., & Zhang, B. (2018). LinkedOmics: Analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Research, 46(D1), D956–D963.

    Article  CAS  PubMed  Google Scholar 

  21. Maragkakis, M., Vergoulis, T., Alexiou, P., Reczko, M., Plomaritou, K., Gousis, M., et al. (2011). DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association. Nucleic Acids Research, 39, 145–8.

    Article  Google Scholar 

  22. Li, R., Deng, Y., Liang, J., Hu, Z., Li, X., Liu, H., et al. (2021). Circular RNA circ-102,166 acts as a sponge of miR-182 and miR-184 to suppress hepatocellular carcinoma proliferation and invasion. Cellular Oncology (Dordrecht), 44(2), 279–295.

    Article  CAS  PubMed  Google Scholar 

  23. Lin, Y., Jiang, M., Chen, W., Zhao, T., & Wei, Y. (2019). Cancer and ER stress: Mutual crosstalk between autophagy, oxidative stress and inflammatory response. Biomedicine Pharmacotherapy, 118, 109249.

    Article  CAS  PubMed  Google Scholar 

  24. Talabnin, K., Talabnin, C., Ishihara, M., & Azadi, P. (2018). Increased expression of the high-mannose M6N2 and NeuAc3H3N3M3N2F tri-antennary N-glycans in cholangiocarcinoma. Oncology Letters, 15(1), 1030–1036.

    PubMed  Google Scholar 

  25. Sun, S. C. (2017). The non-canonical NF-kappaB pathway in immunity and inflammation. Nature Reviews Immunology, 17(9), 545–558.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sirica, A. E. (2011). The role of cancer-associated myofibroblasts in intrahepatic cholangiocarcinoma. Nature Reviews Gastroenterology & Hepatology, 9(1), 44–54.

    Article  Google Scholar 

  27. Zhang, L., Xu, H., Ma, C., Zhang, J., Zhao, Y., Yang, X., et al. (2020). Upregulation of deubiquitinase PSMD14 in lung adenocarcinoma (LUAD) and its prognostic significance. Journal of Cancer, 11(10), 2962–2971.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li, Y., Huang, J., Zeng, B., Yang, D., Sun, J., Yin, X., et al. (2018). PSMD2 regulates breast cancer cell proliferation and cell cycle progression by modulating p21 and p27 proteasomal degradation. Cancer letters, 430, 109–122.

    Article  CAS  PubMed  Google Scholar 

  29. Zhang, Z., Li, H., Zhao, Y., Guo, Q., Yu, Y., Zhu, S., et al. (2019). Asporin promotes cell proliferation via interacting with PSMD2 in gastric cancer. Frontiers in Bioscience, 24, 1178–1189.

    Article  CAS  Google Scholar 

  30. Fang, J., Rhyasen, G., Bolanos, L., Rasch, C., Varney, M., Wunderlich, M., et al. (2012). Cytotoxic effects of bortezomib in myelodysplastic syndrome/acute myeloid leukemia depend on autophagy-mediated lysosomal degradation of TRAF6 and repression of PSMA1. Blood, 120(4), 858–867.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Shitara, A., Shibui, T., Okayama, M., Arakawa, T., Mizoguchi, I., Sakakura, Y., et al. (2013). VAMP4 is required to maintain the ribbon structure of the Golgi apparatus. Molecular and Cellular Biochemistry, 380(1–2), 11–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sun, J., Wang, L., Bao, H., Premi, S., Das, U., Chapman, E. R., et al. (2019). Functional cooperation of alpha-synuclein and VAMP2 in synaptic vesicle recycling. Proceedings of the National Academy of Sciences of the United States of America, 116(23), 11113–11115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ye, Y., Gu, B., Wang, Y., Shen, S., & Huang, W. (2019). E2F1-mediated MNX1-AS1-miR-218-5p-SEC61A1 feedback loop contributes to the progression of colon adenocarcinoma. Journal of Cellular Biochemistry, 120(4), 6145–6153.

    Article  CAS  PubMed  Google Scholar 

  34. Muller-McNicoll, M., Botti, V., de Jesus Domingues, A. M., Brandl, H., Schwich, O. D., Steiner, M. C., et al. (2016). SR proteins are NXF1 adaptors that link alternative RNA processing to mRNA export. Genes & Development, 30(5), 553–566.

    Article  Google Scholar 

  35. Hu, W., Lei, L., Xie, X., Huang, L., Cui, Q., Dang, T., et al. (2019). Heterogeneous nuclear ribonucleoprotein L facilitates recruitment of 53BP1 and BRCA1 at the DNA break sites induced by oxaliplatin in colorectal cancer. Cell Death & Disease, 10(8), 550.

    Article  Google Scholar 

  36. Song, X., Wan, X., Huang, T., Zeng, C., Sastry, N., Wu, B., et al. (2019). SRSF3-Regulated RNA Alternative Splicing Promotes Glioblastoma Tumorigenicity by Affecting Multiple Cellular Processes. Cancer Research, 79(20), 5288–5301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mano, Y., Aishima, S., Fukuhara, T., Tanaka, Y., Kubo, Y., Motomura, T., et al. (2013). Decreased roundabout 1 expression promotes development of intrahepatic cholangiocarcinoma. Human Pathology, 44(11), 2419–2426.

    Article  CAS  PubMed  Google Scholar 

  38. Liu, J., Liu, W., Li, H., Deng, Q., Yang, M., Li, X., et al. (2019). Identification of key genes and pathways associated with cholangiocarcinoma development based on weighted gene correlation network analysis. PeerJ, 7, e7968.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Takahashi, R. U., Prieto-Vila, M., Kohama, I., & Ochiya, T. (2019). Development of miRNA-based therapeutic approaches for cancer patients. Cancer science, 110(4), 1140–1147.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rupaimoole, R., & Slack, F. J. (2017). MicroRNA therapeutics: Towards a new era for the management of cancer and other diseases. Nature Reviews Drug Discovery, 16(3), 203–222.

    Article  CAS  PubMed  Google Scholar 

  41. Han, Y., Meng, F., Venter, J., Wu, N., Wan, Y., Standeford, H., et al. (2016). miR-34a-dependent overexpression of Per1 decreases cholangiocarcinoma growth. Journal of Hepatology, 64(6), 1295–1304.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lampis, A., Carotenuto, P., Vlachogiannis, G., Cascione, L., Hedayat, S., Burke, R., et al. (2018). MIR21 Drives Resistance to Heat Shock Protein 90 Inhibition in Cholangiocarcinoma. Gastroenterology, 154(4), 1066–79.e5.

    Article  CAS  PubMed  Google Scholar 

  43. Pungpapong, V., Zhang, M., & Zhang, D. (2020). Integrating biological knowledge into case-control analysis through iterated conditional modes/medians algorithm. Journal of Computational Biology : A Journal of Computational Molecular Cell Biology, 27(7), 1171–1179.

    Article  CAS  PubMed  Google Scholar 

  44. Khosla, R., Hemati, H., Rastogi, A., Ramakrishna, G., Sarin, S. K., & Trehanpati, N. (2019). miR-26b-5p helps in EpCAM+cancer stem cells maintenance via HSC71/HSPA8 and augments malignant features in HCC. Liver international : Official Journal of the International Association for the Study of the Liver, 39(9), 1692–1703.

    Article  CAS  PubMed  Google Scholar 

  45. Geng, F., Lu, G. F., Ji, M. H., Kong, D. Y., Wang, S. Y., Tian, H., et al. (2019). MicroRNA-26b-3p/ANTXR1 signaling modulates proliferation, migration, and apoptosis of glioma. American Journal of Translational Research, 11(12), 7568–7578.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Xie, T., Pi, G., Yang, B., Ren, H., Yu, J., Ren, Q., et al. (2019). Long non-coding RNA 520 is a negative prognostic biomarker and exhibits pro-oncogenic function in nasopharyngeal carcinoma carcinogenesis through regulation of miR-26b-3p/USP39 axis. Gene, 707, 44–52.

    Article  CAS  PubMed  Google Scholar 

  47. Satoh, J., Kino, Y., & Niida, S. (2015). MicroRNA-Seq data analysis pipeline to identify blood biomarkers for alzheimer’s disease from public data. Biomarker insights, 10, 21–31.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Hong, H., Li, Y., & Su, B. (2017). Identification of circulating mir-125b as a potential biomarker of alzheimer’s disease in APP/PS1 transgenic mouse. Journal of Alzheimer’s Disease : JAD, 59(4), 1449–1458.

    Article  CAS  PubMed  Google Scholar 

  49. Anaparti, V., Smolik, I., Meng, X., Spicer, V., Mookherjee, N., & El-Gabalawy, H. (2017). Whole blood microRNA expression pattern differentiates patients with rheumatoid arthritis, their seropositive first-degree relatives, and healthy unrelated control subjects. Arthritis Research & Therapy, 19(1), 249.

    Article  Google Scholar 

  50. Rizvi, S., & Gores, G. J. (2013). Pathogenesis, diagnosis, and management of cholangiocarcinoma. Gastroenterology, 145(6), 1215–1229.

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported by the National Natural Science Foundation of China, 81802897 and 81901943; the Natural Science Foundation of Guangdong Province, 2021A1515011156, 2021A1515012493; the Operating Foundation of Guangdong Provincial Key Laboratory of Liver Disease Research, 2020B1212060019; the Guangzhou Basic and Applied Basic Research Foundation, 202102020237 and Guangzhou Basic and Applied Basic Research Project Co-funded by Municipal Schools (institutes), 2023A03J0727. Science and Technology Project of Guangzhou, SL2024A03J01216. Scientific Reserch Project of Dongguan Binhaiwan Central Hospital, 2021001.

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Author notes

  1. Xijing Yan, Zhongying Hu and Xuejiao Li contributed equally to this work.

Authors and Affiliations

  1. Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China

    Xijing Yan & Jun Zheng

  2. Department of Breast and Thyroid Surgery, Lingnan Hospital, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China

    Xijing Yan & Kunpeng Hu

  3. Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China

    Zhongying Hu, Xuejiao Li, Jinliang Liang & Rong Li

  4. Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China

    Jiao Gong

  5. Surgical ICU, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China

    Xin Sui

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  1. Xijing Yan
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Contributions

Conception and design: Rong Li, Xijing Yan, Kunpeng Hu, Xin Sui. Administrative support: Rong Li. Provision of study materials or patients: Zhongying Hu, Jinliang Liang, Xuejiao Li, Jiao Gong, Jun Zheng. Collection and assembly of data: Rong Li, Xijing Yan, Zhongying Hu, Jinliang Liang. Data analysis and interpretation: Rong Li, Xijing Yan, Kunpeng Hu, Xin Sui, Zhongying Hu, Jinliang Liang, Jiao Gong, Jun Zheng. Manuscript writing: All authors.

Corresponding authors

Correspondence to Kunpeng Hu, Xin Sui or Rong Li.

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Ethical Statement

Our research was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University. All CCA data involved in this research were retrieved from the online databases (NCBI GEO DataSets and TCGA) which could be confirmed that all written informed consent had already been obtained and the data acquisition were followed the related data access policy and published guidelines.

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Yan, X., Hu, Z., Li, X. et al. Systemic analysis of the prognostic significance and interaction network of miR-26b-3p in cholangiocarcinoma. Appl Biochem Biotechnol (2023). https://doi.org/10.1007/s12010-023-04753-x

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