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2000
Volume 28, Issue 5
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

Aims

This study aimed to construct a prognostic model for papillary renal cell carcinoma (pRCC) utilizing disulfidptosis-associated long non-coding RNAs (lncRNAs). Additionally, it investigated the potential of these lncRNAs in predicting immune responses and drug sensitivity in pRCC.

Background

LncRNAs have been implicated in the progression and prognosis of pRCC. Recently, disulfidptosis, an emerging form of regulated cell death, has shown potential as a therapeutic approach for cancer. However, the potential association between disulfidptosis-related lncRNAs and pRCC remains unclear.

Methods

We analyzed transcriptome profiling and clinical data of pRCC patients from The Cancer Genome Atlas database. Using Pearson correlation analysis, we identified lncRNAs associated with disulfidptosis. Based on the disulfidptosis-related lncRNAs that were correlated with overall survival (OS), we constructed a novel prediction model using least absolute shrinkage and selection operator, univariable Cox regression, and multivariable Cox regression analyses. The model's utility was assessed through Kaplan–Meier survival, receiver operating characteristics, and principal component analyses. Moreover, functional analysis helped identify potential prognostic mechanisms, and the prediction of chemical drugs for pRCC was also performed. Finally, qRT-PCR validated the expression of prognostic lncRNAs in pRCC cells and patient samples.

Results

Our prediction model was based on nine disulfidptosis-related lncRNAs. Evaluation and validation analyses demonstrated that the model had excellent, consistent, and independent prognostic value for pRCC patients, with area under the curve values of 0.954, 0.910, and 0.830 for 1-, 3-, and 5-year OS, respectively. Through functional analysis, we discovered a significant correlation between the identified prognostic signature and immunity. Additionally, in terms of chemotherapy sensitivity, our analysis indicated that the low-risk group exhibited higher sensitivity to sunitinib and pazopanib. Furthermore, the expression patterns of the identified lncRNAs were validated in samples obtained from pRCC cells and patients.

Conclusion

This study successfully established and validated a novel disulfidptosis-related prediction model. The findings suggest the potential involvement of immune-related pathways in lncRNA signature-associated survival. This model holds promise for differentiating prognosis and improving personalized therapeutic strategies for pRCC in clinical practice.

© 2025 Bentham Science Publishers
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2024-04-18
2025-09-17

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References

  1. AngoriS. LoboJ. MochH. Papillary renal cell carcinoma: Current and controversial issues.Curr. Opin. Urol.202232434435110.1097/MOU.0000000000001000 35674688
     [Google Scholar]
  2. AkhtarM. Al-BozomI.A. Al HussainT. Papillary renal cell carcinoma (PRCC): An update.Adv. Anat. Pathol.201926212413210.1097/PAP.0000000000000220 30507616
     [Google Scholar]
  3. MendhirattaN. MurakiP. SiskA.E.Jr ShuchB. Papillary renal cell carcinoma. (Review)Urol. Oncol.202139632733710.1016/j.urolonc.2021.04.013 34034966
     [Google Scholar]
  4. ChenQ. ChengL. LiQ. The molecular characterization and therapeutic strategies of papillary renal cell carcinoma.Expert Rev. Anticancer Ther.201919216917510.1080/14737140.2019.1548939 30474436
     [Google Scholar]
  5. SweeneyP.L. JangA. HalatS.K. PalS.K. BarataP.C. . Advanced papillary renal cell carcinoma: Epidemiology, genomic drivers, current therapies, and ongoing trials. Cancer Treat. Res. Commun., 20223310063910.1016/j.ctarc.2022.100639 36162322
     [Google Scholar]
  6. LiuX. OlszewskiK. ZhangY. LimE.W. ShiJ. ZhangX. ZhangJ. LeeH. KoppulaP. LeiG. ZhuangL. YouM.J. FangB. LiW. MetalloC.M. PoyurovskyM.V. GanB. Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer.Nat. Cell Biol.202022447648610.1038/s41556‑020‑0496‑x 32231310
     [Google Scholar]
  7. JolyJ.H. DelfarahA. PhungP.S. ParrishS. GrahamN.A. A synthetic lethal drug combination mimics glucose deprivation–induced cancer cell death in the presence of glucose.J. Biol. Chem.202029551350136510.1016/S0021‑9258(17)49891‑7 31914417
     [Google Scholar]
  8. LiuX. NieL. ZhangY. YanY. WangC. ColicM. OlszewskiK. HorbathA. ChenX. LeiG. MaoC. WuS. ZhuangL. PoyurovskyM.V. James YouM. HartT. BilladeauD.D. ChenJ. GanB. Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis.Nat. Cell Biol.202325340441410.1038/s41556‑023‑01091‑2 36747082
     [Google Scholar]
  9. YangL. ZhangW. YanY. Identification and characterization of a novel molecular classification based on disulfidptosis-related genes to predict prognosis and immunotherapy efficacy in hepatocellular carcinoma.Aging 202315136135615110.18632/aging.204809 37399661
     [Google Scholar]
  10. WangT. GuoK. ZhangD. WangH. YinJ. CuiH. WuW. Disulfidptosis classification of hepatocellular carcinoma reveals correlation with clinical prognosis and immune profile.Int. Immunopharmacol.202312011036810.1016/j.intimp.2023.110368 37247499
     [Google Scholar]
  11. ChenH. YangW. LiY. MaL. JiZ. Leveraging a disulfidptosis-based signature to improve the survival and drug sensitivity of bladder cancer patients.Front. Immunol.202314119887810.3389/fimmu.2023.1198878 37325625
     [Google Scholar]
  12. ZhaoS. WangL. DingW. YeB. ChengC. ShaoJ. LiuJ. ZhouH. Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework.Front. Endocrinol.202314118040410.3389/fendo.2023.1180404 37152941
     [Google Scholar]
  13. FengZ. ZhaoQ. DingY. XuY. SunX. ChenQ. ZhangY. MiaoJ. ZhuJ. Identification a unique disulfidptosis classification regarding prognosis and immune landscapes in thyroid carcinoma and providing therapeutic strategies.J. Cancer Res. Clin. Oncol.202314913111571117010.1007/s00432‑023‑05006‑4 37347261
     [Google Scholar]
  14. YangL. LiuJ. LiS. LiuX. ZhengF. XuS. FuB. XiongJ. Based on disulfidptosis, revealing the prognostic and immunological characteristics of renal cell carcinoma with tumor thrombus of vena cava and identifying potential therapeutic target AJAP1.J. Cancer Res. Clin. Oncol.2023149129787980410.1007/s00432‑023‑04877‑x 37247081
     [Google Scholar]
  15. GoodallG.J. WickramasingheV.O. RNA in cancer.Nat. Rev. Cancer2021211223610.1038/s41568‑020‑00306‑0 33082563
     [Google Scholar]
  16. ChiY. WangD. WangJ. YuW. YangJ. Long non-coding RNA in the pathogenesis of cancers.Cells201989101510.3390/cells8091015 31480503
     [Google Scholar]
  17. FlippotR. BeinseG. BoilèveA. VibertJ. MaloufG.G. Long non-coding RNAs in genitourinary malignancies: A whole new world.Nat. Rev. Urol.201916848450410.1038/s41585‑019‑0195‑1 31110275
     [Google Scholar]
  18. LvW. LiY. FuL. MengF. LiJ. Linc01133 promotes proliferation and metastasis of human renal cell carcinoma through sponging miR-760.Cell Cycle202221141502151110.1080/15384101.2022.2054250 35446199
     [Google Scholar]
  19. LiuY. ChengG. HuangZ. BaoL. LiuJ. WangC. XiongZ. ZhouL. XuT. LiuD. YangH. ChenK. ZhangX. Long noncoding RNA SNHG12 promotes tumour progression and sunitinib resistance by upregulating CDCA3 in renal cell carcinoma.Cell Death Dis.202011751510.1038/s41419‑020‑2713‑8 32641718
     [Google Scholar]
  20. PuY. DongZ. XiaY. ZhangM. SongJ. HanJ. LncRNA NONHSAT113026 represses renal cell carcinoma tumorigenesis through interacting with NF-κB/p50 and SLUG.Biomed. Pharmacother.2019118109382
     [Google Scholar]
  21. DangR. JinM. NanJ. JiangX. HeZ. SuF. LiD. A novel ferroptosis-related lncRNA signature for prognosis prediction in patients with papillary renal cell carcinoma.Int. J. Gen. Med.20221520722210.2147/IJGM.S341034 35023959
     [Google Scholar]
  22. PangY. WangY. ZhouX. NiZ. ChenW. LiuY. DuW. Cuproptosis-related LncRNA-based prediction of the prognosis and immunotherapy response in papillary renal cell carcinoma.Int. J. Mol. Sci.2023242146410.3390/ijms24021464 36674979
     [Google Scholar]
  23. XieT. LiuB. LiuD. ZhouY. YangQ. WangD. TangM. LiuW. Cuproptosis-related lncRNA signatures predict prognosis and immune relevance of kidney renal papillary cell carcinoma.Front. Pharmacol.202213110398610.3389/fphar.2022.1103986 36618928
     [Google Scholar]
  24. ChenS.H. LinF. ZhuJ.M. KeZ.B. LinT.T. LinY.Z. XueX.Y. WeiY. ZhengQ.S. ChenY.H. XuN. An immune-related lncRNA prognostic model in papillary renal cell carcinoma: A lncRNA expression analysis.Genomics2021113153154010.1016/j.ygeno.2020.09.046 32979493
     [Google Scholar]
  25. MotzerR.J. JonaschE. AgarwalN. AlvaA. BaineM. BeckermannK. CarloM.I. ChoueiriT.K. CostelloB.A. DerweeshI.H. DesaiA. GedY. GeorgeS. GoreJ.L. HaasN. HancockS.L. KapurP. KyriakopoulosC. LamE.T. LaraP.N. LauC. LewisB. MadoffD.C. ManleyB. MichaelsonM.D. MortazaviA. NandagopalL. PlimackE.R. PonskyL. RamalingamS. ShuchB. SmithZ.L. SosmanJ. DwyerM.A. GurskiL.A. MotterA. Kidney cancer, version 3.2022, NCCN clinical practice guidelines in oncology.J. Natl. Compr. Canc. Netw.2022201719010.6004/jnccn.2022.0001 34991070
     [Google Scholar]
  26. de VelascoG. McKayR.R. LinX. MoreiraR.B. SimantovR. ChoueiriT.K. Comprehensive analysis of survival outcomes in non–clear cell renal cell carcinoma patients treated in clinical trials.Clin. Genitourin. Cancer2017156652660.e110.1016/j.clgc.2017.03.004 28410911
     [Google Scholar]
  27. ZhaoQ.J. ZhangJ. XuL. LiuF.F. Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma.World J. Gastroenterol.201824303426343910.3748/wjg.v24.i30.3426 30122881
     [Google Scholar]
  28. WangJ. ZouY. DuB. LiW. YuG. LiL. ZhouL. GuX. SongS. LiuY. ZhouW. XuB. WangZ. SNP-mediated lncRNA-ENTPD3-AS1 upregulation suppresses renal cell carcinoma via miR-155/HIF-1α signaling.Cell Death Dis.202112767210.1038/s41419‑021‑03958‑4 34218253
     [Google Scholar]
  29. YuF. LiangM. WuW. HuangY. ZhengJ. ZhengB. ChenC. Upregulation of long non-coding RNA GCC2-AS1 facilitates malignant phenotypes and correlated with unfavorable prognosis for lung adenocarcinoma.Front. Oncol.20211062860810.3389/fonc.2020.628608 33575222
     [Google Scholar]
  30. LiX. LongJ. ZongL. ZhangC. YangZ. GuoS. ZNF561-AS1 regulates cell proliferation and apoptosis in myocardial infarction through miR-223-3p/NLRP3 axis.Cell Transplant.20223110.1177/09636897221077928 35997481
     [Google Scholar]
  31. SiZ. YuL. JingH. WuL. WangX. Oncogenic lncRNA ZNF561-AS1 is essential for colorectal cancer proliferation and survival through regulation of miR-26a-3p/miR-128-5p-SRSF6 axis.J. Exp. Clin. Cancer Res.20214017810.1186/s13046‑021‑01882‑1 33622363
     [Google Scholar]
  32. JinL. LiC. LiuT. WangL. A potential prognostic prediction model of colon adenocarcinoma with recurrence based on prognostic lncRNA signatures.Hum. Genomics20201412410.1186/s40246‑020‑00270‑8 32522293
     [Google Scholar]
  33. DongZ. YangL. LuJ. GuoY. ShenS. LiangJ. GuoW. Downregulation of LINC00886 facilitates epithelial–mesenchymal transition through SIRT7/ELF3/miR-144 pathway in esophageal squamous cell carcinoma.Clin. Exp. Metastasis202239466167710.1007/s10585‑022‑10171‑w 35616822
     [Google Scholar]
  34. MaB. LuoY. XuW. HanL. LiuW. LiaoT. YangY. WangY. LINC00886 negatively regulates malignancy in anaplastic thyroid cancer.Endocrinology20231644bqac20410.1210/endocr/bqac204 36726346
     [Google Scholar]
  35. XingC. SunS.G. YueZ.Q. BaiF. Role of lncRNA LUCAT1 in cancer.Biomed. Pharmacother.202113411115810.1016/j.biopha.2020.111158
     [Google Scholar]
  36. ZhangC. YangY. WangK. ChenM. LuM. HuC. DuX. XingB. LiuX. The systematic analyses of RING finger gene signature for predicting the prognosis of patients with hepatocellular carcinoma.J. Oncol.2022202211710.1155/2022/2466006 36199791
     [Google Scholar]
  37. SteffensS. JanssenM. RoosF.C. BeckerF. SchumacherS. SeidelC. Incidence and long-term prognosis of papillary compared to clear cell renal cell carcinoma--a multicentre study.Eur. J. Cancer2012481523472352
     [Google Scholar]
  38. Díaz-MonteroC.M. RiniB.I. FinkeJ.H. The immunology of renal cell carcinoma.Nat. Rev. Nephrol.2020161272173510.1038/s41581‑020‑0316‑3 32733094
     [Google Scholar]
  39. SjöbergE. FrödinM. LövrotJ. MezheyeuskiA. JohanssonM. HarmenbergU. EgevadL. SandströmP. ÖstmanA. A minority-group of renal cell cancer patients with high infiltration of CD20+B-cells is associated with poor prognosis.Br. J. Cancer2018119784084610.1038/s41416‑018‑0266‑8 30293996
     [Google Scholar]
  40. DeleuzeA. SaoutJ. DugayF. PeyronnetB. MathieuR. VerhoestG. BensalahK. CrouzetL. LaguerreB. Belaud-RotureauM.A. Rioux-LeclercqN. Kammerer-JacquetS.F. Immunotherapy in renal cell carcinoma: The future is now.Int. J. Mol. Sci.2020217253210.3390/ijms21072532 32260578
     [Google Scholar]
  41. CiccareseC. IacovelliR. BrunelliM. MassariF. BimbattiD. FantinelE. Addressing the best treatment for non-clear cell renal cell carcinoma: A meta-analysis of randomised clinical trials comparing VEGFR-TKis versus mTORi-targeted therapies.Eur. J. Cancer201783237246
     [Google Scholar]
  42. ArmstrongA.J. HalabiS. EisenT. BroderickS. StadlerW.M. JonesR.J. GarciaJ.A. VaishampayanU.N. PicusJ. HawkinsR.E. HainsworthJ.D. KollmannsbergerC.K. LoganT.F. PuzanovI. PickeringL.M. RyanC.W. ProtheroeA. LuskC.M. ObergS. GeorgeD.J. Everolimus versus sunitinib for patients with metastatic non-clear cell renal cell carcinoma (ASPEN): A multicentre, open-label, randomised phase 2 trial.Lancet Oncol.201617337838810.1016/S1470‑2045(15)00515‑X 26794930
     [Google Scholar]
  43. JungK.S. LeeS.J. ParkS.H. LeeJ.L. LeeS.H. LimJ.Y. KangJ.H. LeeS. RhaS.Y. LeeK.H. KimH.Y. LimH.Y. Pazopanib for the treatment of non-clear cell renal cell carcinoma: A single-arm, open-label, multicenter, phase II study.Cancer Res. Treat.201850248849410.4143/crt.2016.584 28546525
     [Google Scholar]
  44. ButiS. BersanelliM. MainesF. FacchiniG. GelsominoF. ZustovichF. SantoniM. VerriE. De GiorgiU. MasiniC. MorelliF. VitaleM.G. SavaT. PratiG. LibriciC. FracconA.P. FornariniG. MaruzzoM. LeonardiF. CaffoO. First-line pazopanib in non–clear-cell renal carcinoma: The Italian retrospective multicenter panorama study.Clin. Genitourin. Cancer2017154e609e61410.1016/j.clgc.2016.12.024 28108284
     [Google Scholar]
  45. GreenerJ.G. KandathilS.M. MoffatL. JonesD.T. A guide to machine learning for biologists.Nat. Rev. Mol. Cell Biol.2022231405510.1038/s41580‑021‑00407‑0 34518686
     [Google Scholar]
  46. SwansonK. WuE. ZhangA. AlizadehA.A. ZouJ. From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.Cell202318681772179110.1016/j.cell.2023.01.035 36905928
     [Google Scholar]
  47. IssaN.T. StathiasV. SchürerS. DakshanamurthyS. Machine and deep learning approaches for cancer drug repurposing.Semin. Cancer Biol.20216813214210.1016/j.semcancer.2019.12.011 31904426
     [Google Scholar]
  48. TranK.A. KondrashovaO. BradleyA. WilliamsE.D. PearsonJ.V. WaddellN. Deep learning in cancer diagnosis, prognosis and treatment selection.Genome Med.202113115210.1186/s13073‑021‑00968‑x
     [Google Scholar]
  49. AladeI.O. ZhangY. XuX. Modeling and prediction of lattice parameters of binary spinel compounds (AM2 X4) using support vector regression with Bayesian optimization.New J. Chem.20214534152551526610.1039/D1NJ01523K
     [Google Scholar]
  50. GoliS. MahjubH. FaradmalJ. MashayekhiH. SoltanianA.R. Survival prediction and feature selection in patients with breast cancer using support vector regression.Comput. Math. Methods Med.2016201611210.1155/2016/2157984 27882074
     [Google Scholar]
  51. ZhangY. XuX. Machine learning steel ms temperature.Simulation202197638342510.1177/0037549721995574
     [Google Scholar]
  52. ZhangY. XuX. Machine learning bioactive compound solubilities in supercritical carbon dioxide.Chem. Phys.202155011129910.1016/j.chemphys.2021.111299
     [Google Scholar]
  53. HossainS.M.M. HalsanaA.A. KhatunL. RayS. MukhopadhyayA. Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model.Sci. Rep.2021111785310.1038/s41598‑021‑87234‑7 33846515
     [Google Scholar]
  54. ZhangY. XuX. Solid particle erosion rate predictions through LSBoost.Powder Technol.202138851752510.1016/j.powtec.2021.04.072
     [Google Scholar]
  55. WangQ. ZhaoY. WangF. TanG. Clustering and machine learning-based integration identify cancer associated fibroblasts genes’ signature in head and neck squamous cell carcinoma.Front. Genet.202314111181610.3389/fgene.2023.1111816 37065499
     [Google Scholar]
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Integrated Bioinformatics and Experimental Validation to Identify a Disulfidptosis-Related lncRNA Model for Prognostic Prediction in Papillary Renal Cell Carcinoma
Comb Chem High Throughput Screen 28, 883 (2025); https://doi.org/10.2174/0113862073303084240403051346
/content/journals/cchts/10.2174/0113862073303084240403051346
/content/journals/cchts/10.2174/0113862073303084240403051346
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Supplements

Supplementary Table S1. Disulfidptosis-related genes. Supplementary Table S2. Clinical characteristics of patients and controls. Supplementary Table S3. Primer sequences used in this study. Supplementary Table S4. Clinical statistical analysis between the training and testing cohorts. Supplementary Table S5. Differentially expressed genes between high and low risk groups.

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  • Article Type:     Research Article
Keyword(s): Disulfidptosis; lncRNA model; papillary renal cell carcinoma; pazopanib; prognostic prediction; sunitinib; tumor immunity

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