Skip to main content

Advertisement

SpringerLink
Log in

Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer

  • Original Paper
  • Published:
Molecular Biotechnology Aims and scope Submit manuscript

Abstract

Pancreatic cancer stands as one of the most lethal malignancies, characterized by delayed diagnosis, high mortality rates, limited treatment efficacy, and poor prognosis. Disulfidptosis, a recently unveiled modality of cell demise induced by disulfide stress, has emerged as a critical player intricately associated with the onset and progression of various cancer types. It has emerged as a promising candidate biomarker for cancer diagnosis, prognosis assessment, and treatment strategies. In this study, we have effectively established a prognostic risk model for pancreatic cancer by incorporating multiple differentially expressed long non-coding RNAs (DElncRNAs) closely linked to disulfide-driven cell death. Our investigation delved into the nuanced relationship between the DElncRNA-based predictive model for disulfide-driven cell death and the therapeutic responses to anticancer agents. Our findings illuminate that the high-risk subgroup exhibits heightened susceptibility to the small molecule compound AZD1208, positioning it as a prospective therapeutic agent for pancreatic cancer. Finally, we have elucidated the underlying mechanistic potential of AZD1208 in ameliorating pancreatic cancer through its targeted inhibition of the peroxisome proliferator-activated receptor-γ (PPARG) protein, employing an array of comprehensive analytical methods, including molecular docking and molecular dynamics (MD) simulations. This study explores disulfidptosis-related genes, paving the way for the development of targeted therapies for pancreatic cancer and emphasizing their significance in the field of oncology. Furthermore, through computational biology approaches, the drug AZD1208 was identified as a potential treatment targeting the PPARG protein for pancreatic cancer. This discovery opens new avenues for exploring targets and screening drugs for pancreatic cancer.

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The datasets generated and analysed in this study are available from the corresponding authors on request.

References

  1. Zhao, Z., & Liu, W. (2020). Pancreatic cancer: A review of risk factors, diagnosis, and treatment. Technology in Cancer Research & Treatment, 19, 1533033820962117.

    Article  CAS  Google Scholar 

  2. Moore, A., & Donahue, T. (2019). Pancreatic cancer. JAMA, 322(144), 1426.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Di Martino, M., & El Boghdady, M. (2023). Pancreatic cancer surgery. BMC Surgery, 23(1), 196.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mizrahi, J. D., Surana, R., Valle, J. W., & Shroff, R. T. (2020). Pancreatic cancer. The Lancet, 395(10242), 2008–2020.

    Article  CAS  Google Scholar 

  5. Traub, B., Link, K.-H., & Kornmann, M. (2021). Curing pancreatic cancer. Seminars in Cancer Biology, 76, 232–246.

    Article  CAS  PubMed  Google Scholar 

  6. Kleeff, J., Korc, M., Apte, M., La Vecchia, C., Johnson, C. D., Biankin, A. V., Neale, R. E., Tempero, M., Tuveson, D. A., Hruban, R. H., & Neoptolemos, J. P. (2016). Pancreatic cancer. Nature Reviews Disease Primers, 2, 16022.

    Article  PubMed  Google Scholar 

  7. Qi, C., Ma, J., Sun, J., Wu, X., & Ding, J. (2023). The role of molecular subtypes and immune infiltration characteristics based on disulfidptosis-associated genes in lung adenocarcinoma. Aging-US, 15(11), 5075–5095.

    CAS  Google Scholar 

  8. Li, L., Jun, L., Qianbao, L., Jinzhi, H., Yuanfeng, C., Cuiyi, F., Yaoyao, L., Fukun, C., & Zhouyan, W. (2023). Disulfidptosis-associated LncRNAs index predicts prognosis and chemotherapy drugs sensitivity in cervical cancer. Scientific Reports, 13(1), 12470.

    Article  Google Scholar 

  9. Liu, X., Zhang, Y., Zhuang, L., Olszewski, K., & Gan, B. (2020). NADPH debt drives redox bankruptcy: SLC7A11/xCT-mediated cystine uptake as a double-edged sword in cellular redox regulation. Genes & Diseases, 8(6), 731–745.

    Article  Google Scholar 

  10. Hengrui, L., & Tao, T. (2023). Pan-cancer genetic analysis of disulfidptosis-related gene set. Cancer Genetics, 278–279, 91–103.

    Google Scholar 

  11. Yuxin, C., Wanying, X., Yuting, Z., Yu, G., & Yuanyuan, W. (2023). A novel disulfidptosis-related immune checkpoint genes signature: Forecasting the prognosis of hepatocellular carcinoma. Journal of Cancer Research and Clinical Oncology, 149(14), 12843–12854.

    Article  Google Scholar 

  12. Xing, F., Qin, Y., Xu, J., Wang, W., & Zhang, B. (2023). Construction of a novel disulfidptosis-related lncRNA prognostic signature in pancreatic cancer. Molecular Biotechnology. https://doi.org/10.1007/s12033-023-00875-z

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fernandez-Diaz, J., Beteta-Gobel, R., Torres, M., Cabot, J., Fernandez-Garcia, P., Llado, V., Escriba, P. V., & Busquets, X. (2021). Tri-2-hydroxyarachidonein induces cytocidal autophagy in pancreatic ductal adenocarcinoma cancer cell models. Frontiers in Physiology, 12, 782525.

    Article  PubMed  Google Scholar 

  14. Zhong, Z., Xu, M., & Tan, J. (2022). Identification of an oxidative stress-related LncRNA signature for predicting prognosis and chemotherapy in patients with hepatocellular carcinoma. Pathology & Oncology Research, 28, 1610670.

    Article  CAS  Google Scholar 

  15. Wu, X., Liang, Y., Chen, X., Long, X., Xu, W., Liu, L., Wang, B., & Zou, X. (2022). Identification of survival risk and immune-related characteristics of kidney renal clear cell carcinoma. Journal of Immunology Research, 2022, 6149369.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Liu, X., Nie, L., Zhang, Y., Yan, Y., Wang, C., Colic, M., Olszewski, K., Horbath, A., Chen, X., Lei, G., Mao, C., Wu, S., Zhuang, L., Poyurovsky, M. V., James You, M., Hart, T., Billadeau, D. D., Chen, J., & Gan, B. (2023). Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nature Cell Biology, 25(3), 404–414.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen, H., Yang, W., Li, Y., Ma, L., & Ji, Z. (2023). Leveraging a disulfidptosis-based signature to improve the survival and drug sensitivity of bladder cancer patients. Frontiers in Immunology, 14, 1198878.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rao, G., Pan, H., Sheng, X., & Liu, J. (2022). Prognostic value of stem cell index-related characteristics in primary hepatocellular carcinoma. Contrast Media & Molecular Imaging. https://doi.org/10.1155/2022/2672033

    Article  Google Scholar 

  20. Wang, Y., Huang, S., Zhang, Y., Cheng, Y., Dai, L., Gao, W., Feng, Z., Tao, J., & Zhang, Y. (2023). Construction and validation of a prognostic model based on autophagy-related genes for hepatocellular carcinoma in the Asian population. BMC Genomics, 24(1), 357.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ren, D., Wang, W.-L., Wang, G., Chen, W.-W., Li, X.-K., Li, G.-D., Bai, S.-X., Dong, H.-M., & Chen, W.-H. (2022). Development and internal validation of a nomogram-based model to predict three-year and five-year overall survival in patients with stage II/III colon cancer. Cancer Management and Research, 14, 225–236.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ruan, R., Chen, S., Tao, Y., Yu, J., Zhou, D., Cui, Z., Shen, Q., & Wang, S. (2021). A nomogram for predicting lymphovascular invasion in superficial esophageal squamous cell carcinoma. Frontiers in Oncology, 11, 663802.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Russo, S., Li, G., & Villez, K. (2019). Automated model selection in principal component analysis: A new approach based on the cross-validated ignorance score. Industrial & Engineering Chemistry Research, 58(30), 13448–13468.

    Article  CAS  Google Scholar 

  24. Xiaoting, T., Weitao, H., Wei, Y., & Taiyong, F. (2023). Exploration of key ferroptosis-related genes and immune infiltration in Crohn’s disease using bioinformatics. Scientific Reports, 13(1), 12769.

    Article  Google Scholar 

  25. Innis, S. E., Reinaltt, K., Civelek, M., & Anderson, W. D. (2021). GSEAplot: A package for customizing gene set enrichment analysis in R. Journal of Computational Biology, 28(6), 629–631.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Guan, M., Jiao, Y., & Zhou, L. (2022). Immune infiltration analysis with the CIBERSORT method in lung cancer. Disease Markers. https://doi.org/10.1155/2022/3186427

    Article  PubMed  PubMed Central  Google Scholar 

  27. Liu, S., Tang, Q., Huang, J., Zhan, M., Zhao, W., Yang, X., Li, Y., Qiu, L., Zhang, F., Lu, L., & He, X. (2021). Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data. Aging-US, 13(8), 11257–11280.

    Article  CAS  Google Scholar 

  28. Geeleher, P., Cox, N. J., & Huang, R. (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 

  29. Kong, X., Liu, C., Lu, P., Guo, Y., Zhao, C., Yang, Y., Bo, Z., Wang, F., Peng, Y., & Meng, J. (2021). Combination of UPLC–Q-TOF/MS and network pharmacology to reveal the mechanism of Qizhen decoction in the treatment of colon cancer. ACS Omega, 6(22), 14341–14360.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, X.-W., Zhang, C.-A., & Ye, M. (2022). Study on the mechanism of Xiaotan Sanjie recipe in the treatment of colon cancer based on network pharmacology. BioMed Research International. https://doi.org/10.1155/2022/9498109

    Article  PubMed  PubMed Central  Google Scholar 

  31. Xinhao, C., Qilei, L., & Lei, Z. (2023). An accurate and universal protein-small molecule batch docking solution using Autodock Vina. Results in Engineering. https://doi.org/10.1016/j.rineng.2023.101335

    Article  Google Scholar 

  32. Yuan, S., Chan, H. C. S., & Hu, Z. (2017). Using PyMOL as a platform for computational drug design. Wiley Interdisciplinary Reviews, 7(2), e1298.

    Google Scholar 

  33. Pantaleão, S. Q., Fernandes, P. O., Gonçalves, J. E., Maltarollo, V. G., & Honorio, K. M. (2021). Recent advances in the prediction of pharmacokinetics properties in drug design studies: A review. ChemMedChem, 17(1), e202100542.

    Article  PubMed  Google Scholar 

  34. Dong, J., Wang, N.-N., Yao, Z.-J., Zhang, L., Cheng, Y., Ouyang, D., Lu, A.-P., & Cao, D.-S. (2018). ADMETlab: A platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. Journal of Cheminformatics, 10(1), 29.

    Article  PubMed  PubMed Central  Google Scholar 

  35. He, X., Man, V. H., Yang, W., Lee, T.-S., & Wang, J. (2020). A fast and high-quality charge model for the next generation general AMBER force field. The Journal of Chemical Physics, 135(11), 114502.

    Article  Google Scholar 

  36. Harris, J. A., Liu, R., Martins de Oliveira, V., Vázquez-Montelongo, E. A., Henderson, J. A., & Shen, J. (2022). GPU-accelerated all-atom particle-Mesh Ewald continuous constant pH molecular dynamics in amber. Journal of Chemical Theory and Computation, 18(12), 7510–7527.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bisht, A., Tewari, D., Kumar, S., & Chandra, S. (2023). Network pharmacology, molecular docking, and molecular dynamics simulation to elucidate the mechanism of anti-aging action of Tinospora cordifolia. Molecular Diversity. https://doi.org/10.1007/s11030-023-10684-w

    Article  PubMed  Google Scholar 

  38. Tuo, Y., Tang, Y., Yang, R., Zhao, X., Luo, M., Zhou, X., & Wang, Y. (2023). Virtual screening and biological activity evaluation of novel efflux pump inhibitors targeting AdeB. International Journal of Biological Macromolecules, 250, 126109.

    Article  CAS  PubMed  Google Scholar 

  39. Yousaf, M. A., Anwer, S. A., Basheera, S., & Sivanandan, S. (2023). Computational investigation of Moringa oleifera phytochemicals targeting EGFR: Molecular docking, molecular dynamics simulation and density functional theory studies. Journal of Biomolecular Structure and Dynamics. https://doi.org/10.1021/acs.accounts.3c00193

    Article  PubMed  Google Scholar 

  40. Suresh, C. H., & Anila, S. (2023). Molecular electrostatic potential topology analysis of noncovalent interactions. Accounts of Chemical Research, 56(13), 1884–1895.

    Article  CAS  PubMed  Google Scholar 

  41. Pereira, F., Xiao, K., Latino, D. A. R. S., Wu, C., Zhang, Q., & Aires-de-Sousa, J. (2016). Machine learning methods to predict density functional theory B3LYP energies of HOMO and LUMO orbitals. Journal of Chemical Information and Modeling, 57(1), 11–21.

    Article  PubMed  Google Scholar 

  42. Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M., & Alizadeh, A. A. (2018). Profiling tumor infiltrating immune cells with CIBERSORT. Methods in Molecular Biology, 1711, 243–259.

    Article  CAS  PubMed  Google Scholar 

  43. Rodrigues, T., Reker, D., Schneider, P., & Schneider, G. (2016). Counting on natural products for drug design. Nature Chemistry, 8(6), 531–541.

    Article  CAS  PubMed  Google Scholar 

  44. Ramharack, P., & Soliman, M. E. S. (2018). Bioinformatics-based tools in drug discovery: The cartography from single gene to integrative biological networks. Drug Discovery Today, 23(9), 1658–1665.

    Article  CAS  PubMed  Google Scholar 

  45. Ravinder, S., Gunpreet, K., Parveen, B., Viney, C., & Vikas, G. (2023). Bioinformatics paradigms in drug discovery and drug development. Current Topics in Medicinal Chemistry, 23(7), 579–588.

    Article  Google Scholar 

  46. Wooller, S. K., Benstead-Hume, G., Chen, X., Ali, Y., & Pearl, F. M. G. (2017). Bioinformatics in translational drug discovery. Bioscience Reports. https://doi.org/10.1042/BSR20160180

  47. Ayse Tarbin, J., Ayse, Yilmaz G., Mine, S., Deepak, M., Subodh, N. B., Vinay, Y., Hatice, Y. Mahmut., Hülya, Çelik. O., Nilüfer, B., Venkatesan, J., & N. Amaç Fatih TuYu,. (2023). Cytotoxic activity of quinolinequinones in cancer: In vitro studies, molecular docking, and ADME/PK profiling. Chemical Biology & Drug Design, 102(5), 1133–1154.

    Article  Google Scholar 

  48. Ashiru, M. A., Ogunyemi, S. O., Temionu, O. R., Ajibare, A. C., Cicero-Mfon, N. C., Ihekuna, O. A., Jagun, M. O., Abdulmumin, L., Adisa, Q. K., Asibor, Y. E., Okorie, C. J., Lawal, M. O., Babalola, M. O., Abdulrasaq, I. T., Salau, L. B., Olatunji, I. O., Bankole, M. A., Daud, A. B., & Adeyemi, A. O. (2023). Identification of EGFR inhibitors as potential agents for cancer therapy: Pharmacophore-based modeling, molecular docking, and molecular dynamics investigations. Journal of Molecular Modeling, 29(5), 128.

    Article  CAS  PubMed  Google Scholar 

  49. Bhardwaj, P., Biswas, G. P., Mahata, N., Ghanta, S., & Bhunia, B. (2022). Exploration of binding mechanism of triclosan towards cancer markers using molecular docking and molecular dynamics. Chemosphere, 293, 133550.

    Article  CAS  PubMed  Google Scholar 

  50. Qayoom, H., Mehraj, U., Sofi, S., Aisha, S., Almilaibary, A., Alkhanani, M., & Mir, M. A. (2022). Expression patterns and therapeutic implications of CDK4 across multiple carcinomas: A molecular docking and MD simulation study. Medical Oncology, 39(10), 158.

    Article  CAS  PubMed  Google Scholar 

  51. Makki, A. A., Ibraheem, W., & Alzain, A. A. (2023). Cytosporone E analogues as BRD4 inhibitors for cancer treatment: Molecular docking and molecular dynamic investigations. Journal of Biomolecular Structure and Dynamics, 41(22), 12643–12653.

    Article  CAS  PubMed  Google Scholar 

  52. Arjmand, B., Hamidpour, S. K., Alavi-Moghadam, S., Yavari, H., Shahbazbadr, A., Tavirani, M. R., Gilany, K., & Larijani, B. (2022). Molecular docking as a therapeutic approach for targeting cancer stem cell metabolic processes. Frontiers in Pharmacology, 13, 892656.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wang, Z., He, R., Dong, S., & Zhou, W. (2023). Pancreatic stellate cells in pancreatic cancer: As potential targets for future therapy. Frontiers in Oncology, 13, 1185093.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Chen, X., Zeh, H. J., Kang, R., Kroemer, G., & Tang, D. (2021). Cell death in pancreatic cancer: From pathogenesis to therapy. Nature Reviews Gastroenterology & Hepatology, 18(11), 804–823.

    Article  Google Scholar 

  55. Liu, X., Zhuang, L., & Gan, B. (2023). Disulfidptosis: Disulfide stress–induced cell death. Trends in Cell Biology. https://doi.org/10.1016/j.tcb.2023.07.009

  56. Liu, X., Olszewski, K., Zhang, Y., Lim, E. W., Shi, J., Zhang, X., Zhang, J., Lee, H., Koppula, P., Lei, G., Zhuang, L., You, M. J., Fang, B., Li, W., Metallo, C. M., Poyurovsky, M. V., & Gan, B. (2020). Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer. Nature Cell Biology, 22(4), 476–486.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lee, M., Lee, K. H., Min, A., Kim, J., Kim, S., Jang, H., Lim, J. M., Kim, S. H., Ha, D. H., Jeong, W. J., Suh, K. J., Yang, Y. W., Kim, T. Y., Oh, D. Y., Bang, Y. J., & Im, S. A. (2019). Pan-Pim kinase inhibitor AZD1208 suppresses tumor growth and synergistically interacts with Akt inhibition in gastric cancer cells. Cancer Research and Treatment, 51(2), 451–463.

    Article  CAS  PubMed  Google Scholar 

  58. Park, Y. K., Obiang-Obounou, B. W., Lee, K. B., Choi, J. S., & Jang, B. C. (2018). AZD1208, a pan-Pim kinase inhibitor, inhibits adipogenesis and induces lipolysis in 3T3-L1 adipocytes. Journal of Cellular and Molecular Medicine, 22(4), 2488–2497.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Pazienza, V., Tavano, F., Francavilla, M., Fontana, A., Pellegrini, F., Benegiamo, G., Corbo, V., di Mola, F. F., Di Sebastiano, P., Andriulli, A., & Mazzoccoli, G. (2012). Time-qualified patterns of variation of PPARγ, DNMT1, and DNMT3B expression in pancreatic cancer cell lines. PPAR Research. https://doi.org/10.1155/2012/890875

    Article  PubMed  PubMed Central  Google Scholar 

  60. Toffoli, B., Gilardi, F., Winkler, C., Soderberg, M., Kowalczuk, L., Arsenijevic, Y., Bamberg, K., Bonny, O., & Desvergne, B. (2017). Nephropathy in Pparg-null mice highlights PPARgamma systemic activities in metabolism and in the immune system. PLoS ONE, 12(2), e0171474.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Li, L., Fu, J., Liu, D., Sun, J., Hou, Y., Chen, C., Shao, J., Wang, L., Wang, X., Zhao, R., Wang, H., Andersen, M. E., Zhang, Q., Xu, Y., & Pi, J. (2020). Hepatocyte-specific Nrf2 deficiency mitigates high-fat diet-induced hepatic steatosis: Involvement of reduced PPARγ expression. Redox Biology, 30, 101412.

    Article  CAS  PubMed  Google Scholar 

  62. Abrego, J., Sanford-Crane, H., Oon, C., Xiao, X., Betts, C. B., Sun, D., Nagarajan, S., Diaz, L., Sandborg, H., Bhattacharyya, S., Xia, Z., Coussens, L. M., Tontonoz, P., & Sherman, M. H. (2022). A cancer cell-intrinsic GOT2-PPARdelta axis suppresses antitumor immunity. Cancer Discovery, 12(10), 2414–2433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Schmidt, M. V., Brune, B., & von Knethen, A. (2010). The nuclear hormone receptor PPARgamma as a therapeutic target in major diseases. The Scientific World Journal, 10, 2181–2197.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Liu, J., Wang, Y., & Lin, L. (2019). Small molecules for fat combustion: Targeting obesity. Acta Pharmaceutica Sinica B, 9(2), 220–236.

    Article  PubMed  Google Scholar 

  65. Ning, Z., Guo, X., Liu, X., Lu, C., Wang, A., Wang, X., Wang, W., Chen, H., Qin, W., Liu, X., Zhou, L., Ma, C., Du, J., Lin, Z., Luo, H., Otkur, W., Qi, H., Chen, D., Xia, T., … Piao, H. L. (2022). USP22 regulates lipidome accumulation by stabilizing PPARgamma in hepatocellular carcinoma. Nature Communications, 13(1), 2187.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Dicitore, A., Caraglia, M., Gaudenzi, G., Manfredi, G., Amato, B., Mari, D., Persani, L., Arra, C., & Vitale, G. (2014). Type I interferon-mediated pathway interacts with peroxisome proliferator activated receptor-γ (PPAR-γ): At the cross-road of pancreatic cancer cell proliferation. Biochimica et Biophysica Acta (BBA), 1845(1), 42–52.

    CAS  PubMed  Google Scholar 

  67. Sabatino, L., Fucci, A., Pancione, M., & Colantuoni, V. (2012). PPARG epigenetic deregulation and its role in colorectal tumorigenesis. PPAR Research, 2012, 687492.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Yan, J., Yang, H., Wang, G., Sun, L., Zhou, Y., Guo, Y., Xi, Z., & Jiang, X. (2010). Autophagy augmented by troglitazone is independent of EGFR transactivation and correlated with AMP-activated protein kinase signaling. Autophagy, 6(1), 67–73.

    Article  CAS  PubMed  Google Scholar 

  69. Wang, Y. L., & Miao, Q. (2008). To live or to die: Prosurvival activity of PPARgamma in cancers. PPAR Research, 2008, 209629.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kristiansen, G., Jacob, J., Buckendahl, A.-C., Grützmann, R., Alldinger, I., Sipos, B., Klöppel, Gn., Bahra, M., Langrehr, J. M., Neuhaus, P., Dietel, M., & Pilarsky, C. (2006). Peroxisome proliferator-activated receptor γ is highly expressed in pancreatic cancer and is associated with shorter overall survival times. Clinical Cancer Research, 12(21), 6444–6451.

    Article  CAS  PubMed  Google Scholar 

  71. Pazienza, V., Tavano, F., Benegiamo, G., Vinciguerra, M., Burbaci, F. P., Copetti, M., di Mola, F. F., Andriulli, A., & di Sebastiano, P. (2012). Correlations among PPARgamma, DNMT1, and DNMT3B expression levels and pancreatic cancer. PPAR Research, 2012, 461784.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Liu, Y., Deguchi, Y., Wei, D., Liu, F., Moussalli, M. J., Deguchi, E., Li, D., Wang, H., Valentin, L. A., Colby, J. K., Wang, J., Zheng, X., Ying, H., Gagea, M., Ji, B., Shi, J., Yao, J. C., Zuo, X., & Shureiqi, I. (2022). Rapid acceleration of KRAS-mutant pancreatic carcinogenesis via remodeling of tumor immune microenvironment by PPARdelta. Nature Communications, 13(1), 2665.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Rui, Y., Han, X., Jiang, A., Hu, J., Li, M., Liu, B., Qian, F., & Huang, L. (2022). Eucalyptol prevents bleomycin-induced pulmonary fibrosis and M2 macrophage polarization. European Journal of Pharmacology, 931, 175184.

    Article  CAS  PubMed  Google Scholar 

  74. Han, L., Bai, L., Qu, C., Dai, E., Liu, J., Kang, R., Zhou, D., Tang, D., & Zhao, Y. (2021). PPARG-mediated ferroptosis in dendritic cells limits antitumor immunity. Biochemical and Biophysical Research Communications, 576, 33–39.

    Article  CAS  PubMed  Google Scholar 

  75. Jang, E. J., Lee, D. H., Im, S.-S., Yee, J., & Gwak, H. S. (2023). Correlation between PPARG Pro12Ala polymorphism and therapeutic responses to thiazolidinediones in patients with type 2 diabetes: A meta-analysis. Pharmaceutics, 15(6), 1778.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Luo, Y., Yang, Y., Liu, M., Wang, D., Wang, F., Bi, Y., Ji, J., Li, S., Liu, Y., Chen, R., Huang, H., Wang, X., Swidnicka-Siergiejko, A. K., Janowitz, T., Beyaz, S., Wang, G., Xu, S., Bialkowska, A. B., Luo, C. K., … Lu, W. (2019). Oncogenic KRAS reduces expression of FGF21 in acinar cells to promote pancreatic tumorigenesis in mice on a high-fat diet. Gastroenterology, 157(5), 1413–1428.

    Article  CAS  PubMed  Google Scholar 

  77. Wang, Z., Shen, W., Li, X., Feng, Y., Qian, K., Wang, G., Gao, Y., Xu, X., Zhang, S., Yue, L., & Cao, J. (2020). The PPARγ agonist rosiglitazone enhances the radiosensitivity of human pancreatic cancer cells. Drug Design, Development and Therapy, 14, 3099–3110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This research was supported by National Natural Science Foundation of China (No. 82100684) and Natural Science Foundation of Chongqing, China (CSTB2022NSCQ-MSX1493).

Author information

Authors and Affiliations

  1. School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 405400, People’s Republic of China

    Hongtao Duan, Li Gao, Aiminuer Asikaer, Lingzhi Liu, Kuilong Huang & Yan Shen

Authors
  1. Hongtao Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aiminuer Asikaer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingzhi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kuilong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yan Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HD made significant contributions to the development of research methods and experimental protocols, as well as authored the manuscript. LG was responsible for data compilation. AA and LL assisted in revising the manuscript. KH provided critical insights into data analysis and interpretation. YS supervised and planned the entire research project.

Corresponding author

Correspondence to Yan Shen.

Ethics declarations

Competing interest

The authors of this research manuscript declare that they have no competing interests associated with this work. This includes but is not limited to financial, personal, or professional conflicts that could potentially bias the research or its interpretation. The research was conducted in an unbiased and impartial manner, and there are no financial or other relationships that might influence the content or findings presented in this manuscript.

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 (DOCX 1567 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

Duan, H., Gao, L., Asikaer, A. et al. Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer. Mol Biotechnol (2024). https://doi.org/10.1007/s12033-024-01131-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12033-024-01131-8

Keywords

Search

Navigation