Abstract
Glioblastoma (GBM) is the most malignant and aggressive primary brain tumor and is highly resistant to current therapeutic strategies. Previous studies have demonstrated that reactive oxygen species (ROS) play an important role in the regulation of signal transduction and immunosuppressive environment in GBM. To further study the role of ROS in prognosis, tumor micro-environment (TME) and immunotherapeutic response in GBM, an ROS-related nine-gene signature was constructed using the Lasso-Cox regression method and validated using three other datasets in our research, based on the hallmark ROS-pathway-related gene sets and the Cancer Genome Atlas GBM dataset. Differences in prognosis, TME scores, immune cell infiltration, immune checkpoint expression levels, and drug sensitivity between high-risk and low-risk subgroups were analyzed using R software. Collectively, our research uncovered a novel ROS-related prognostic model for primary GBM, which could prove to be a potential tool for clinical diagnosis of GBM, and help assess the immune and molecular characteristics of ROS in the tumorigenesis and immunosuppression of GBM. Our research also revealed that the expressions of ROS-related genes—HSPB1, LSP1, and PTX3—were closely related to the cell markers of tumor-associated macrophages (TAMs) and M2 macrophages validated by quantitative RT-PCR, suggesting them could be potential targets of immunotherapy for GBM.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The GBM RNA-seq data and corresponding clinical information were observed from the TCGA (https://portal.gdc.cancer.gov/) and CGGA (http://www.cgga.org.cn). The GBM microarray data and corresponding clinical information were retrieved from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and the TCGA. The single-cell RNA-seq dataset (dataset ID: GSE131928) was analyzed and downloaded from Single Cell Portal website (https://singlecell.broadinstitute.org/single_cell). The ROS-related gene list was downloaded from the MSigDB website (https://www.gsea-msigdb.org/gsea/msigdb).
References
Aldoss, I. T., Tashi, T., & Ganti, A. K. (2009). Seliciclib in malignancies. Expert Opinion on Investigational Drugs, 18(12), 1957–1965. https://doi.org/10.1517/13543780903418445
Blanche, P., Dartigues, J.-F., & Jacqmin-Gadda, H. (2013). Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Statistics in Medicine, 32(30), 5381–5397. https://doi.org/10.1002/sim.5958
Bonavita, E., Gentile, S., Rubino, M., Maina, V., Papait, R., Kunderfranco, P., Greco, C., Feruglio, F., Molgora, M., Laface, I., & Tartari, S. (2015). PTX3 is an extrinsic oncosuppressor regulating complement-dependent inflammation in cancer. Cell, 160(4), 700–714. https://doi.org/10.1016/j.cell.2015.01.004
Cao, M., Cai, J., Yuan, Y., Shi, Y., Wu, H., Liu, Q., Yao, Y., Chen, L., Dang, W., Zhang, X., Xiao, J., Yang, K., He, Z., Yao, X., Cui, Y., Zhang, X., & Bian, X. (2019). A four-gene signature-derived risk score for glioblastoma: prospects for prognostic and response predictive analyses. Cancer Biology & Medicine, 16, 595–605. https://doi.org/10.20892/j.issn.2095-3941.2018.0277
Cao, J.-Y., Guo, Q., Guan, G. F., Zhu, C., Zou, C.-Y., Zhang, L.-Y., Cheng, W., Wang, G. L., Cheng, P., Wu, A. H., & Li, G. Y. (2020). Elevated lymphocyte specific protein 1 expression is involved in the regulation of leukocyte migration and immunosuppressive microenvironment in glioblastoma. Aging, 12(2), 1656–1684. https://doi.org/10.18632/aging.102706
Chakravarti, A., Wang, M., Robins, H. I., Lautenschlaeger, T., Curran, W. J., Brachman, D. G., Schultz, C. J., Choucair, A., Dolled-Filhart, M., Christiansen, J., & Gustavson, M. (2013). RTOG 0211: A phase 1/2 study of radiation therapy with concurrent gefitinib for newly diagnosed glioblastoma patients International. Journal of Radiation Oncology Biology Physics, 85(5), 1206–1211. https://doi.org/10.1016/j.ijrobp.2012.10.008
Chang, C. Y., Pan, P. H., Wu, C. C., Liao, S. L., Chen, W. Y., Kuan, Y. H., Wang, W.-Y., & Chen, C.-J. (2021). Endoplasmic reticulum stress contributes to gefitinib-induced apoptosis in glioma. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms22083934
Clara, J. A., Monge, C., Yang, Y., & Takebe, N. (2019). Targeting signalling pathways and the immune microenvironment of cancer stem cells—A clinical update. Nature Reviews Clinical Oncology, 17(4), 204–232. https://doi.org/10.1038/s41571-019-0293-2
Deng, S., Zheng, Y., Mo, Y., Xu, X., Li, Y., Zhang, Y., Liu, J., Chen, J., Tian, Y., & Ke, Y. (2021). Ferroptosis suppressive genes correlate with immunosuppression in glioblastoma. World Neurosurgery, 152, e436–e448. https://doi.org/10.1016/j.wneu.2021.05.098
Fan, F., Zhang, H., Dai, Z., Zhang, Y., Xia, Z., Cao, H., Yang, K., Hu, S., Guo, Y., Ding, F., & Cheng, Q. (2021). A comprehensive prognostic signature for glioblastoma patients based on transcriptomics and single cell sequencing. Cellular Oncology, 44(4), 917–935. https://doi.org/10.1007/s13402-021-00612-1
Federico, M., Symonds, C. E., Bagella, L., Rizzolio, F., Fanale, D., Russo, A., & Giordano, A. (2010). R-Roscovitine (Seliciclib) prevents DNA damage-induced cyclin A1 upregulation and hinders non-homologous end-joining (NHEJ) DNA repair. Molecular Cancer, 9, 208. https://doi.org/10.1186/1476-4598-9-208
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software. https://doi.org/10.18637/jss.v033.i01
Garnier, D., Ratcliffe, E., Briand, J., Cartron, P. F., Oliver, L., & Vallette, F. M. (2022). The activation of mesenchymal stem cells by glioblastoma microvesicles alters their exosomal secretion of miR-100–5p, miR-9–5p and let-7d-5p. Biomedicines. https://doi.org/10.3390/biomedicines10010112
Geeleher, P., Cox, N., & Huang, R. S. (2014). pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One, 9(9), e107468. https://doi.org/10.1371/journal.pone.0107468
Giacomini, A., Ghedini, G. C., Presta, M., & Ronca, R. (2018). Long pentraxin 3: A novel multifaceted player in cancer. Biochimica Et Biophysica Acta - Reviews on Cancer, 1869(1), 53–63. https://doi.org/10.1016/j.bbcan.2017.11.004
Guo, X., Qiu, W., Liu, Q., Qian, M., Wang, S., Zhang, Z., Gao, X., Chen, Z., Xue, H., & Li, G. (2018). Immunosuppressive effects of hypoxia-induced glioma exosomes through myeloid-derived suppressor cells via the miR-10a/Rora and miR-21/Pten pathways. Oncogene, 37(31), 4239–4259. https://doi.org/10.1038/s41388-018-0261-9
Gutmann, D. H. (2020). The sociobiology of brain tumor. Advances in experimental medicine and biology (pp. 115–125). London: Springer International Publishing.
Herb, M., & Schramm, M. (2021). Functions of ROS in macrophages and antimicrobial immunity. Antioxidants (basel, Switzerland), 10(2), 313. https://doi.org/10.3390/antiox10020313
Herbst, R. S., & Kies, M. S. (2003). Gefitinib: Current and future status in cancer therapy. Clinical Advances in Hematology & Oncology, 1(8), 466–472.
Huang, H., Zhang, S., Li, Y., Liu, Z., Mi, L., Cai, Y., Wang, X., Chen, L., Ran, H., Xiao, D., & Li, F. (2021). Suppression of mitochondrial ROS by prohibitin drives glioblastoma progression and therapeutic resistance. Nature Communications, 12(1), 3720–3720. https://doi.org/10.1038/s41467-021-24108-6
Jia, D., Li, S., Li, D., Xue, H., Yang, D., & Liu, Y. (2018). Mining TCGA database for genes of prognostic value in glioblastoma microenvironment. Aging, 10(4), 592–605. https://doi.org/10.18632/aging.101415
Jia, H., Truica, C. I., Wang, B., Wang, Y., Ren, X., Harvey, H. A., Song, J., & Yang, J. M. (2017). Immunotherapy for triple-negative breast cancer: Existing challenges and exciting prospects. Drug Resistance Updates, 32(1), 15. https://doi.org/10.1016/j.drup.2017.07.002
Kreisl, T. N., Lassman, A. B., Mischel, P. S., Rosen, N., Scher, H. I., Teruya-Feldstein, J., Shaffer, D., Lis, E., & Abrey, L. E. (2009). A pilot study of everolimus and gefitinib in the treatment of recurrent glioblastoma (GBM). Journal of Neuro-Oncology, 92(1), 99–105. https://doi.org/10.1007/s11060-008-9741-z
Lambiv, W. L., Vassallo, I., Delorenzi, M., Shay, T., Diserens, A. C., Misra, A., Feuerstein, B., Murat, A., Migliavacca, E., Hamou, M. F., & Sciuscio, D. (2011). The Wnt inhibitory factor 1 (WIF1) is targeted in glioblastoma and has a tumor suppressing function potentially by induction of senescence. Neuro-Oncology, 13(7), 736–747. https://doi.org/10.1093/neuonc/nor036
Lee, J. H., Lee, J. E., Kahng, J. Y., Kim, S. H., Park, J. S., Yoon, S. J., Um, J. Y., Kim, W. K., Lee, J. K., Park, J., & Kim, E. H. (2018). Human glioblastoma arises from subventricular zone cells with low-level driver mutations. Nature, 560(7717), 243–247. https://doi.org/10.1038/s41586-018-0389-3
Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., Li, B., & Liu, X. S. (2020). TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Researrch, 48(1), 509–514. https://doi.org/10.1093/nar/gkaa407
Liang, P., Chai, Y., Zhao, H., & Wang, G. (2020). Predictive analyses of prognostic-related immune genes and immune infiltrates for glioblastoma. Diagnostics (Basel, Switzerland), 10(3), 177. https://doi.org/10.3390/diagnostics10030177
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015). The molecular signatures database (MSigDB) hallmark gene set collection. Cell Systems, 1(6), 417–425. https://doi.org/10.1016/j.cels.2015.12.004
Lin, W., Shen, P., Song, Y., Huang, Y., & Tu, S. (2021). Reactive oxygen species in autoimmune cells: Function, differentiation, and metabolism. Frontiers in Immunology, 12, 635021–635021. https://doi.org/10.3389/fimmu.2021.635021
Long, S., Peng, F., Song, B., Wang, L., Chen, J., & Shang, B. (2021). Heat shock protein beta 1 is a prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma. Int J Gen Med, 14, 5483–5492. https://doi.org/10.2147/ijgm.S330608
Mo, X., Zheng, Z., He, Y., Zhong, H., Kang, X., Shi, M., Liu, T., Jiao, Z., & Huang, Y. (2018). Antiglioma via regulating oxidative stress and remodeling tumor-associated macrophage using lactoferrin-mediated biomimetic codelivery of simvastatin/fenretinide. Journal of Controlled Release, 287, 12–23. https://doi.org/10.1016/j.jconrel.2018.08.012
Murat, A., Migliavacca, E., Gorlia, T., Lambiv, W. L., Shay, T., Hamou, M. F., De Tribolet, N., Regli, L., Wick, W., Kouwenhoven, M. C., & Hainfellner, J. A. (2008). Stem cell-related “self-renewal” signature and high epidermal growth factor receptor expression associated with resistance to concomitant chemoradiotherapy in glioblastoma. Journal of Clinical Oncology, 26(18), 3015–3024. https://doi.org/10.1200/jco.2007.15.7164
Neftel, C., Laffy, J., Filbin, M. G., Hara, T., Shore, M. E., Rahme, G. J., Richman, A. R., Silverbush, D., Shaw, M. L., Hebert, C. M., Dewitt, J., Gritsch, S., Perez, E. M., Castro, L. N. G., Lan, X., Druck, N., Rodman, C., Dionne, D., Kaplan, A., & Suvà, M. L. (2019). An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell, 178(4), 835-849.e821. https://doi.org/10.1016/j.cell.2019.06.024
Nemoto, T., Kanai, T., Yanagita, T., Satoh, S., Maruta, T., Yoshikawa, N., Kobayashi, H., & Wada, A. (2008). Regulation of Akt mRNA and protein levels by glycogen synthase kinase-3beta in adrenal chromaffin cells: effects of LiCl and SB216763. European Journal of Pharmacology, 586(1–3), 82–89. https://doi.org/10.1016/j.ejphar.2008.02.075
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12(5), 453–457. https://doi.org/10.1038/nmeth.3337
Patrick, S., Gowda, P., Lathoria, K., Suri, V., & Sen, E. (2021). YAP1-mediated regulation of mitochondrial dynamics in IDH1 mutant gliomas. Journal of Cell Science. https://doi.org/10.1242/jcs.259188
Petterson, S. A., Sørensen, M. D., & Kristensen, B. W. (2020). Expression profiling of primary and recurrent glioblastomas reveals a reduced level of Pentraxin 3 in recurrent glioblastomas. Journal of Neuropathology & Experimental Neurology, 79(9), 975–985. https://doi.org/10.1093/jnen/nlaa088
Rajesh, Y., Biswas, A., Banik, P., Pal, I., Das, S., Borkar, S. A., Sardana, H., Saha, A., Das, S. K., Emdad, L., Fisher, P. B., & Man-dal, M. (2020). Transcriptional regulation of HSPB1 by Friend leukemia integration-1 factor modulates radiation and temozolomide resistance in glioblastoma. Oncotarget, 11(13), 1097–1108. https://doi.org/10.18632/oncotarget.27425
Renee Bailey, L., & Janas, M. (2003). Getting to grips with gefitinib. The Lancet Oncology, 4(12), 719–720. https://doi.org/10.1016/s1470-2045(05)70063-2
Roux, C., Jafari, S. M., Shinde, R., Duncan, G., Cescon, D. W., Silvester, J., Chu, M. F., Hodgson, K., Berger, T., Wakeham, A., & Palomero, L. (2019). Reactive oxygen species modulate macrophage immunosuppressive phenotype through the up-regulation of PD-L1. Proceedings of the National Academy of Sciences of the United States of America, 116(10), 4326–4335. https://doi.org/10.1073/pnas.1819473116
Rubino, M., Kunderfranco, P., Basso, G., Greco, C. M., Pasqualini, F., Serio, S., Roncalli, M., Laghi, L., Mantovani, A., Papait, R., & Garlanda, C. (2017). Epigenetic regulation of the extrinsic oncosuppressor PTX3 gene in inflammation and cancer. Oncoimmunology, 6(7), e1333215. https://doi.org/10.1080/2162402x.2017.1333215
Salazar-Ramiro, A., Ramírez-Ortega, D., Pérez de la Cruz, V., Hérnandez-Pedro, N. Y., González-Esquivel, D. F., Sotelo, J., & Pineda, B. (2016). Role of redox status in development of glioblastoma. Frontiers in Immunology, 7, 156–156. https://doi.org/10.3389/fimmu.2016.00156
Sampson, J. H., Gunn, M. D., Fecci, P. E., & Ashley, D. M. (2020). Brain immunology and immunotherapy in brain tumours. Nature Reviews. Cancer, 20(1), 12–25. https://doi.org/10.1038/s41568-019-0224-7
Şengelen, A., & Önay-Uçar, E. (2018). Rosmarinic acid and siRNA combined therapy represses Hsp27 (HSPB1) expression and induces apoptosis in human glioma cells. Cell Stress and Chaperones, 23(5), 885–896. https://doi.org/10.1007/s12192-018-0896-z
Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2011). Regularization paths for Cox’s proportional hazards model via coordinate descent. Journal of Statistical Software, 39(5), 1–13. https://doi.org/10.18637/jss.v039.i05
Singer, E., Judkins, J., Salomonis, N., Matlaf, L., Soteropoulos, P., McAllister, S., & Soroceanu, L. (2015). Reactive oxygen species-mediated therapeutic response and resistance in glioblastoma. Cell Death & Disease, 6(1), e1601–e1601. https://doi.org/10.1038/cddis.2014.566
Sun, W., Yan, J., Ma, H., Wu, J., & Zhang, Y. (2022). Autophagy-dependent ferroptosis-related signature is closely associated with the prognosis and tumor immune escape of patients with glioma. International Journal of General Medicine, 15, 253–270. https://doi.org/10.2147/ijgm.S343046
Tan, N., Liu, J., Li, P., Sun, Z., Pan, J., & Zhao, W. (2019). Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma. Aging, 11(23), 11010–11029. https://doi.org/10.18632/aging.102506
Tran, A. N., Walker, K., Harrison, D. G., Chen, W., Mobley, J., Hocevar, L., Hackney, J. R., Sedaka, R. S., Pollock, J. S., Goldberg, M. S., & Hambardzumyan, D. (2018). Reactive species balance via GTP cyclohydrolase I regulates glioblastoma growth and tumor initiating cell maintenance. Neuro-Oncology, 20(8), 1055–1067. https://doi.org/10.1093/neuonc/noy012
Tung, J. N., Ko, C. P., Yang, S. F., Cheng, C. W., Chen, P. N., Chang, C. Y., Lin, C.-L., Yang, T.-F., Hsieh, Y.-H., & Chen, K.-C. (2016). Inhibition of pentraxin 3 in glioma cells impairs proliferation and invasion in vitro and in vivo. Journal of Neuro-Oncology, 129(2), 201–209. https://doi.org/10.1007/s11060-016-2168-z
Van Woensel, M., Mathivet, T., Wauthoz, N., Rosière, R., Garg, A. D., Agostinis, P., Mathieu, V., Kiss, R., Lefranc, F., Boon, L., Belmans, J., Van Gool, S. W., Gerhardt, H., Amighi, K., & De Vleeschouwer, S. (2017). Sensitization of glioblastoma tumor micro-environment to chemo- and immunotherapy by Galectin-1 intranasal knock-down strategy. Scientific Reports, 7(1), 1217–1217. https://doi.org/10.1038/s41598-017-01279-1
Villanueva, M. T. (2020). Immunocytokines turn up the heat in glioblastoma. Nature Reviews Drug Discovery, 19(12), 836–836. https://doi.org/10.1038/d41573-020-00188-9
Yang, M., Oh, I. Y., Mahanty, A., Jin, W.-L., & Yoo, J. S. (2020). Immunotherapy for glioblastoma: current state, challenges, and future perspectives. Cancers, 12(9), 2334. https://doi.org/10.3390/cancers12092334
Ye, H., Huang, H., Cao, F., Chen, M., Zheng, X., & Zhan, R. (2016). HSPB1 enhances SIRT2-mediated G6PD activation and promotes glioma cell proliferation. PLoS ONE, 11(10), e0164285. https://doi.org/10.1371/journal.pone.0164285
Treviño, V., Shen, H., Laird, P. W., Levine, D. A., Carter, S. L., Getz, G., Stemke-Hale, K., Mills, G. B., & Verhaak, R. G. W. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications, 4, 2612–2612. https://doi.org/10.1038/ncomms3612
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012). clusterProfiler: An R package for comparing biological themes among gene clusters. Omics : A Journal of Integrative Biology, 16(5), 284–287. https://doi.org/10.1089/omi.2011.0118
Zhang, Y., Choksi, S., Chen, K., Pobezinskaya, Y., Linnoila, I., & Liu, Z.-G. (2013). ROS play a critical role in the differentiation of alternatively activated macrophages and the occurrence of tumor-associated macrophages. Cell Research, 23(7), 898–914. https://doi.org/10.1038/cr.2013.75
Zhao, D., Zhang, H., Uyar, R., Hossain, J. A., Miletic, H., Tonn, J.-C., Glass, R., & Kälin, R. E. (2021). Comparing tumor cell invasion and myeloid cell composition in compatible prifmary and relapsing glioblastoma. Cancers, 13(14), 3636. https://doi.org/10.3390/cancers13143636
Zhu, X., Zhou, Y., Ou, Y., Cheng, Z., Han, D., Chu, Z., & Pan, S. (2021). Characterization of ferroptosis signature to evaluate the predict prognosis and immunotherapy in glioblastoma. Aging, 13(13), 17655–17672. https://doi.org/10.18632/aging.203257
Acknowledgements
We would like to acknowledge the researchers’ contribution to the TCGA, CGGA, GEO and everyone who contributed to this article.
Funding
This research was funded by The National Natural Science Foundation of China (No. 82171832), the Natural Science Foundation of Zhejiang (No. LQ19H030001) and Shanghai Science and Technology development Foundation (No. 20Z11900100).
Author information
Authors and Affiliations
Contributions
Conceptualization, CL and JLY; Data curation, JLZ; Formal analysis, JLY; Investigation, ZPW; Methodology, PK; Project administration, HRC; Resources, JLZ; Software, JLY; Validation, CL; Visualization, ZPW; Writing—original draft, Prashant Kaushal; Writing—review & editing, JLY.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Ethical Approval
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Shanghai Tongji Hospital affiliated to the Tongji University (Shanghai, China). (Protocol code: 2020-KYSB-190-XZ-210526 and 2021.05.26).
Consent to Participate
Informed consent was obtained from all subjects involved in the study.
Consent to Publish
NA.
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.
12017_2022_8719_MOESM1_ESM.jpg
Supplementary file1 (JPG 4208 kb) The functional enrichment analysis of the 197 differentially expressed ROS-related genes. (A-C) The GO enrichment analyses of the 197 differentially expressed ROS-related genes. (D) The KEGG enrichment analysis of the 197 differentially expressed ROS-related genes. (E-G) The network diagram of the GO enrichment analyses for the 197 differentially expressed ROS-related genes. (H) The network diagram of the KEGG enrichment analysis of the 197 differentially expressed ROS-related genes
12017_2022_8719_MOESM2_ESM.jpg
Supplementary file2 (JPG 2928 kb) The correlation analyses among the nine hub genes, 22 immune cells and immune-related scores in three validation cohorts
12017_2022_8719_MOESM3_ESM.jpg
Supplementary file3 (JPG 1492 kb) The calibration plots of the training cohort and the three validation cohorts. (A) The calibration plots of the training cohort. (B-D) The calibration plots of the three validation cohorts
12017_2022_8719_MOESM4_ESM.jpg
Supplementary file4 (JPG 1517 kb) 18 drugs with higher sensitivity in the low-risk subgroup compared with high-risk subgroup (p < 0.001) by using the R package “pRRophetic”
12017_2022_8719_MOESM5_ESM.xlsx
Supplementary file5 (XLSX 254 kb) The list of 979 ROS-related genes collected from the hallmark reactive oxygen species pathway gene set of Molecular Signatures Database v7.4, and other thirteen related gene sets
12017_2022_8719_MOESM6_ESM.xlsx
Supplementary file6 (XLSX 395 kb) The functional enrichment analysis of the 197 differentially expressed ROS- related genes.
12017_2022_8719_MOESM7_ESM.xlsx
Supplementary file7 (XLSX 10 kb) 15 differentially expressed ROS-related genes associated with the survival of primary GBM patients (p < 0.05)
12017_2022_8719_MOESM9_ESM.xlsx
Supplementary file9 (XLSX 425 kb) 4615 differentially expressed genes between primary GBM samples and normal samples, among which, 2981 genes were upregulated, and 1634 genes were downregulated in primary GBM samples compared with normal samples (p < 0.05, |log2FC| ≥ 2)
12017_2022_8719_MOESM10_ESM.xlsx
Supplementary file10 (XLSX 26 kb) 197 differentially expressed ROS-related mRNAs, among which 124 genes were upregulated and 73 were downregulated in primary GBM samples compared with normal samples
Rights and permissions
About this article
Cite this article
Kaushal, P., Zhu, J., Wan, Z. et al. Prognosis and Immune Landscapes in Glioblastoma Based on Gene-Signature Related to Reactive-Oxygen-Species. Neuromol Med 25, 102–119 (2023). https://doi.org/10.1007/s12017-022-08719-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12017-022-08719-w