Treffer: Construction and validation of a machine learning-based immune-related prognostic model for glioma.
Title:
Construction and validation of a machine learning-based immune-related prognostic model for glioma.
Authors:
Mao Q; Department of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China., Qiao Z; Department of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China., Wang Q; Department of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China., Zhao W; Department of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China., Ju H; Department of Neurosurgery, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China. Juhaitao2023@163.com.
Source:
Journal of cancer research and clinical oncology [J Cancer Res Clin Oncol] 2024 Oct 01; Vol. 150 (10), pp. 439. Date of Electronic Publication: 2024 Oct 01.
Publication Type:
Journal Article; Validation Study; Retracted Publication
Language:
English
Journal Info:
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 7902060 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-1335 (Electronic) Linking ISSN: 01715216 NLM ISO Abbreviation: J Cancer Res Clin Oncol Subsets: MEDLINE
Imprint Name(s):
Original Publication: Berlin ; New York : Springer-Verlag.
MeSH Terms:
Comments:
Retraction in: J Cancer Res Clin Oncol. 2025 Jan 25;151(2):49. doi: 10.1007/s00432-025-06099-9. (PMID: 39862261)
References:
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Booth TC, Williams M, Luis A, Cardoso J, Ashkan K, Shuaib H (2020) Machine learning and glioma imaging biomarkers. Clin Radiol 75(1):20–32. https://doi.org/10.1016/j.crad.2019.07.001 Epub 2019 Jul 29. (PMID: 10.1016/j.crad.2019.07.00131371027)
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Faridah IS, Yusmazura Z, Muhammad LM, Nik DN, Tan SC (2024) SF1: a standardised fraction of Clinacanthus nutans that inhibits the Stemness properties of Cancer Stem-Like cells derived from Cervical Cancer. Sains Malaysiana 53(3):667–679. https://doi.org/10.17576/jsm-2024-5303-14. (PMID: 10.17576/jsm-2024-5303-14)
Gao X, Leone GW, Wang H (2020) Cyclin D-CDK4/6 functions in cancer. Adv Cancer Res 148:147–169. https://doi.org/10.1016/bs.acr.2020.02.002. (PMID: 10.1016/bs.acr.2020.02.00232723562)
Garcia-Fabiani MB, Ventosa M, Comba A, Candolfi M, Nicola Candia AJ, Alghamri MS et al (2020) Immunotherapy for gliomas: shedding light on progress in preclinical and clinical development. Expert Opin Investig Drugs 29(7):659–684 Epub 2020 Jun 4. (PMID: 10.1080/13543784.2020.176852832400216)
Gillard AG, Shin DH, Hampton LA, Lopez-Rivas A, Parthasarathy A, Fueyo J et al (2024) Targeting innate immunity in Glioma Therapy. Int J Mol Sci. https://doi.org/10.3390/ijms25020947. (PMID: 10.3390/ijms250209473825602110815900)
Goel S, DeCristo MJ, Watt AC, BrinJones H, Sceneay J, Li BB et al (2020) CDK4/6 inhibition suppresses tumour growth and enhances the effect of temozolomide in glioma cells. J Cell Mol Med 24(9):5135–5145. https://doi.org/10.1038/nature23465. (PMID: 10.1038/nature23465)
Goel S, Bergholz JS, Zhao JJ (2022) Targeting CDK4 and CDK6 in cancer. Nat Rev Cancer 22(6):356–372. https://doi.org/10.1038/s41568-022-00456-3. (PMID: 10.1038/s41568-022-00456-3353046049149100)
Gong L, Ji L, Xu D, Wang J, Zou J (2021) TGF-β links glycolysis and immunosuppression in glioblastoma. Histol Histopathol 36(11):1111–1124. https://doi.org/10.14670/HH-18-366. (PMID: 10.14670/HH-18-36634323284)
Han Y, Wang Y, Dong X, Sun D, Liu Z, Yue J et al (2023) TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res 51(D1):D1425–d1431. https://doi.org/10.1093/nar/gkac959. (PMID: 10.1093/nar/gkac95936321662)
Heinz A (2020) Elastases and elastokines: elastin degradation and its significance in health and disease. Crit Rev Biochem Mol Biol 55(3):252–273. https://doi.org/10.1080/10409238.2020.1768208. (PMID: 10.1080/10409238.2020.176820832530323)
Hoeman C, Shen C, Becher OJ (2018) CDK4/6 and PDGFRA signaling as therapeutic targets in diffuse intrinsic pontine glioma. Front Oncol 8:191. https://doi.org/10.3389/fonc.2018.00191. (PMID: 10.3389/fonc.2018.00191299046235990603)
Hongyu G, Heng Z, Junchao C, Jueheng W, Jie Y, Jun L et al (2011) IKBKE is over-expressed in glioma and contributes to resistance of glioma cells to apoptosis via activating NF-κB. J Pathol 223(3):436–445. https://doi.org/10.1002/path.2815. (PMID: 10.1002/path.2815)
Jia-Hua H, Juan W, Yuan-Zhong Y, Qun-Xi C, Li-Ling L, Lu S et al (2021) SSTR2 is a prognostic factor and a promising therapeutic target in glioma. Am J Transl Res 13(10):11223–11234.
Jin S, Shigang L, Miaojing W, Xianggan W, Yan D, Yansheng L et al (2020) HOTAIR-EZH2 inhibitor AC1Q3QWB upregulates CWF19L1 and enhances cell cycle inhibition of CDK4/6 inhibitor palbociclib in glioma. Clin Transl Med 10(1):182–198. https://doi.org/10.1002/ctm2.21. (PMID: 10.1002/ctm2.21)
Jung S, Rutka JT, Hinek A (1998) Tropoelastin and elastin degradation products promote proliferation of human astrocytoma cell lines. J Neuropathol Exp Neurol 57(5):439–448. (PMID: 10.1097/00005072-199805000-000079596414)
Jung S, Hinek A, Tsugu A, Hubbard SL, Ackerley C, Becker LE et al (1999) Astrocytoma cell interaction with elastin substrates: implications for astrocytoma invasive potential. Glia 25(2):179–189. https://doi.org/10.1002/(sici)1098-1136(19990115)25. (PMID: 10.1002/(sici)1098-1136(19990115)259890632)
Karlsson M, Zhang C, Méar L, Zhong W, Digre A, Katona B et al (2021) A single-cell type transcriptomics map of human tissues. Sci Adv. https://doi.org/10.1126/sciadv.abh2169. (PMID: 10.1126/sciadv.abh2169349364528694624)
Kazunori T, Masao Y, Daisuke A, Junya S, Hiroyuki O, Takashi S et al (2020) Lymphovascular invasion in early gastric cancer: impact of ancillary D2-40 and elastin staining on interobserver agreement. Histopathology 76(6):888–897. https://doi.org/10.1111/his.14075. (PMID: 10.1111/his.14075)
Kocher M, Ruge MI, Galldiks N, Lohmann P (2020) Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol 196(10):856–867. https://doi.org/10.1007/s00066-020-01626-8 Epub 2020 May 11. (PMID: 10.1007/s00066-020-01626-8323941007498494)
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Grant Information:
NJYT24030 Research Institute of Mongolian Medicine of Inner Mongolia Autonomous Region
Contributed Indexing:
Keywords: Glioma; Immune microenvironment; Immune-related genes; Machine learning; Prognosis model
Substance Nomenclature:
0 (Biomarkers, Tumor)
Entry Date(s):
Date Created: 20241001 Date Completed: 20241001 Latest Revision: 20250125
Update Code:
20250125
PubMed Central ID:
PMC11445300
DOI:
10.1007/s00432-024-05970-5
PMID:
39352539
Database:
MEDLINE
Weitere Informationen
Background: Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine.
Methods: Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model's performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments.
Results: In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden. The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration.
Conclusion: This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy.
(© 2024. The Author(s).)