REFERENCES
1. Quail DF, Bowman RL, Akkari L, et al. The tumor microenvironment underlies acquired resistance to CSF-1R inhibition in gliomas. Science 2016;352:aad3018.
2. Weenink B, French PJ, Sillevis Smitt PAE, Debets R, Geurts M. Immunotherapy in glioblastoma: current shortcomings and future perspectives. Cancers (Basel) 2020;12:751.
3. Xu S, Tang L, Li X, Fan F, Liu Z. Immunotherapy for glioma: current management and future application. Cancer Lett 2020;476:1-12.
4. Sun X, Bao J, Shao Y. Mathematical modeling of therapy-induced cancer drug resistance: connecting cancer mechanisms to population survival rates. Sci Rep 2016;6:22498.
5. Jin MZ, Jin WL. The updated landscape of tumor microenvironment and drug repurposing. Signal Transduct Target Ther 2020;5:166.
6. Pérez-Ruiz E, Melero I, Kopecka J, Sarmento-Ribeiro AB, García-Aranda M, De Las Rivas J. Cancer immunotherapy resistance based on immune checkpoints inhibitors: Targets, biomarkers, and remedies. Drug Resist Updat 2020;53:100718.
7. Khalaf K, Hana D, Chou JT, Singh C, Mackiewicz A, Kaczmarek M. Aspects of the tumor microenvironment involved in immune resistance and drug resistance. Front Immunol 2021;12:656364.
8. Wu P, Gao W, Su M, et al. Adaptive mechanisms of tumor therapy resistance driven by tumor microenvironment. Front Cell Dev Biol 2021;9:641469.
9. Bai R, Chen N, Li L, et al. Mechanisms of cancer resistance to immunotherapy. Front Oncol 2020;10:1290.
11. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási AL. The large-scale organization of metabolic networks. Nature 2000;407:651-4.
12. Verhaak RG, Hoadley KA, Purdom E, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010;17:98-110.
13. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-40.
14. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 2012;40:4288-97.
15. Chen Y, Lun AT, Smyth GK. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res 2016;5:1438.
16. Sun X, Liu X, Xia M, Shao Y, Zhang XD. Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas. J Transl Med 2019;17:159.
17. He X, Sun X, Shao Y. Network-based survival analysis to discover target genes for developing cancer immunotherapies and predicting patient survival. J Appl Stat 2021;48:1352-73.
18. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559.
19. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005;4:Article17.
20. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 2008;24:719-20.
21. Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10:1523.
22. Gönen M, Heller G. Concordance probability and discriminatory power in proportional hazards regression. Biometrika 2005;92: 965–70. Available from: http://www.jstor.org/stable/20441249[Last accessed on 24 Apr 2022].
23. Zhang Y, Shao Y. Concordance measure and discriminatory accuracy in transformation cure models. Biostatistics 2018;19:14-26.
24. Simon N, Friedman J, Hastie T and Tibshirani R. SGL: Fit a GLM (or Cox Model) with a combination of lasso and group lasso regularization. R package version 1.3. 2019. Available from: https://CRAN.R-project.org/package=SGL[Last accessed on 24 Apr 2022].
25. Kamarudin AN, Cox T, Kolamunnage-Dona R. Time-dependent ROC curve analysis in medical research: current methods and applications. BMC Med Res Methodol 2017;17:53.
26. Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 2013;32:5381-97.
27. Greten FR, Grivennikov SI. Inflammation and cancer: triggers, mechanisms, and consequences. Immunity 2019;51:27-41.
28. Fu R, Shen Q, Xu P, Luo JJ, Tang Y. Phagocytosis of microglia in the central nervous system diseases. Mol Neurobiol 2014;49:1422-34.
29. Henke E, Nandigama R, Ergün S. Extracellular matrix in the tumor microenvironment and its impact on cancer therapy. Front Mol Biosci 2019;6:160.
30. Claus EB, Walsh KM, Wiencke JK, et al. Survival and low-grade glioma: the emergence of genetic information. Neurosurg Focus 2015;38:E6.
31. Brat DJ, Verhaak RG, Aldape KD, et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 2015;372:2481-98.
32. Cannarile MA, Weisser M, Jacob W, Jegg AM, Ries CH, Rüttinger D. Colony-stimulating factor 1 receptor (CSF1R) inhibitors in cancer therapy. J Immunother Cancer 2017;5:53.
33. Andersen BM, Faust Akl C, Wheeler MA, Chiocca EA, Reardon DA, Quintana FJ. Glial and myeloid heterogeneity in the brain tumour microenvironment. Nat Rev Cancer 2021;21:786-802.
34. Gonzalez H, Hagerling C, Werb Z. Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes Dev 2018;32:1267-84.
35. Berraondo P, Sanmamed MF, Ochoa MC, et al. Cytokines in clinical cancer immunotherapy. Br J Cancer 2019;120:6-15.
37. Otto T, Sicinski P. Cell cycle proteins as promising targets in cancer therapy. Nat Rev Cancer 2017;17:93-115.
38. Dominguez-Brauer C, Thu KL, Mason JM, Blaser H, Bray MR, Mak TW. Targeting mitosis in cancer: emerging strategies. Mol Cell 2015;60:524-36.
39. Zhou M, Bracci PM, McCoy LS, et al. Serum macrophage-derived chemokine/CCL22 levels are associated with glioma risk, CD4 T cell lymphopenia and survival time. Int J Cancer 2015;137:826-36.
40. Martinenaite E, Munir Ahmad S, Hansen M, et al. CCL22-specific T Cells: Modulating the immunosuppressive tumor microenvironment. Oncoimmunology 2016;5:e1238541.
41. Wainwright DA, Dey M, Chang A, Lesniak MS. Targeting tregs in malignant brain cancer: overcoming IDO. Front Immunol 2013;4:116.
42. Deng L, Xiong P, Luo Y, Bu X, Qian S, Zhong W. Bioinformatics analysis of the molecular mechanism of diffuse intrinsic pontine glioma. Oncol Lett 2016;12:2524-30.
43. Abbas SZ, Qadir MI, Muhammad SA. Systems-level differential gene expression analysis reveals new genetic variants of oral cancer. Sci Rep 2020;10:14667.
44. Velpula KK, Bhasin A, Asuthkar S, Tsung AJ. Combined targeting of PDK1 and EGFR triggers regression of glioblastoma by reversing the Warburg effect. Cancer Res 2013;73:7277-89.
46. Wang Z, Xu X, Liu N, et al. SOX9-PDK1 axis is essential for glioma stem cell self-renewal and temozolomide resistance. Oncotarget 2018;9:192-204.
47. Luo D, Xu X, Li J, et al. The PDK1/c-Jun pathway activated by TGF-
48. Signore M, Pelacchi F, di Martino S, et al. Combined PDK1 and CHK1 inhibition is required to kill glioblastoma stem-like cells in vitro and in vivo. Cell Death Dis 2014;5:e1223.
49. Peng F, Wang J, Fan W, et al. Glycolysis gatekeeper PDK1 reprograms breast cancer stem cells under hypoxia. Oncogene 2018;37:1062-74.
50. Chen Y, Meng Z, Zhang L, Liu F. CD2 Is a novel immune-related prognostic biomarker of invasive breast carcinoma that modulates the tumor microenvironment. Front Immunol 2021;12:664845.
51. Naeim F, Rao N, Song SX, Phan RT. Principles of Immunophenotyping. In: Naeim F, Rao N, Song SX, Phan RT. Atlas of hematopathology: morphology, immunophenotype, cytogenetics, and molecular approaches (2nd edition). Cambridge: Academic Press; 2018. pp. 29–56.
52. Han J, Choi YL, Kim H, et al. MMP11 and CD2 as novel prognostic factors in hormone receptor-negative, HER2-positive breast cancer. Breast Cancer Res Treat 2017;164:41-56.
53. Tsai HF, Chang YC, Li CH, et al. Type V collagen alpha 1 chain promotes the malignancy of glioblastoma through PPRC1-ESM1 axis activation and extracellular matrix remodeling. Cell Death Discov 2021;7:313.
54. Gu S, Peng Z, Wu Y, et al. COL5A1 serves as a biomarker of tumor progression and poor prognosis and may be a potential therapeutic target in gliomas. Front Oncol 2021;11:752694.
55. Fu X, Zhang P, Song H, et al. LTBP1 plays a potential bridge between depressive disorder and glioblastoma. J Transl Med 2020;18:391.
56. Pencheva N, de Gooijer MC, Vis DJ, et al. Identification of a druggable pathway controlling glioblastoma invasiveness. Cell Rep 2017;20:48-60.
57. Snijders AM, Lee SY, Hang B, Hao W, Bissell MJ, Mao JH. FAM83 family oncogenes are broadly involved in human cancers: an integrative multi-omics approach. Mol Oncol 2017;11:167-79.
58. Walian PJ, Hang B, Mao JH. Prognostic significance of FAM83D gene expression across human cancer types. Oncotarget 2016;7:3332-40.
59. Dong C, Fan W, Fang S. PBK as a potential biomarker associated with prognosis of glioblastoma. J Mol Neurosci 2020;70:56-64.
60. Han Z, Li L, Huang Y, Zhao H, Luo Y. PBK/TOPK: A therapeutic target worthy of attention. Cells 2021;10:371.
61. Joel M, Mughal AA, Grieg Z, et al. Targeting PBK/TOPK decreases growth and survival of glioma initiating cells in vitro and attenuates tumor growth in vivo. Mol Cancer 2015;14:121.
62. Mao P, Bao G, Wang YC, et al. PDZ-Binding kinase-dependent transcriptional regulation of CCNB2 promotes tumorigenesis and radio-resistance in glioblastoma. Transl Oncol 2020;13:287-94.
63. Patil AA, Sayal P, Depondt ML, et al. FANCD2 re-expression is associated with glioma grade and chemical inhibition of the Fanconi Anaemia pathway sensitises gliomas to chemotherapeutic agents. Oncotarget 2014;5:6414-24.
64. Bravo-Navas S, Yáñez L, Romón Í, Pipaón C. Elevated FANCA expression determines a worse prognosis in chronic lymphocytic leukemia and interferes with p53 function. FASEB J 2019;33:10477-89.
65. Ferrer M, de Winter JP, Mastenbroek DC, et al. Chemosensitizing tumor cells by targeting the Fanconi anemia pathway with an adenovirus overexpressing dominant-negative FANCA. Cancer Gene Ther 2004;11:539-46.
66. Pan SJ, Zhan SK, Ji WZ, et al. Ubiquitin-protein ligase E3C promotes glioma progression by mediating the ubiquitination and degrading of Annexin A7. Sci Rep 2015;5:11066.
67. Ferrarese R, Harsh GR 4th, Yadav AK, et al. Lineage-specific splicing of a brain-enriched alternative exon promotes glioblastoma progression. J Clin Invest 2014;124:2861-76.
68. Yadav AK, Renfrow JJ, Scholtens DM, et al. Monosomy of chromosome 10 associated with dysregulation of epidermal growth factor signaling in glioblastomas. JAMA 2009;302:276-89.
69. Bielle F, Di Stefano AL, Meyronet D, et al. Diffuse gliomas with FGFR3-TACC3 fusion have characteristic histopathological and molecular features. Brain Pathol 2018;28:674-83.
70. Lasorella A, Sanson M, Iavarone A. FGFR-TACC gene fusions in human glioma. Neuro Oncol 2017;19:475-83.
71. Mata DA, Benhamida JK, Lin AL, et al. Genetic and epigenetic landscape of IDH-wildtype glioblastomas with FGFR3-TACC3 fusions. Acta Neuropathol Commun 2020;8:186.
72. Wang Y, Liang D, Chen J, et al. Targeted therapy with anlotinib for a patient with an Oncogenic FGFR3-TACC3 fusion and recurrent glioblastoma. Oncologist 2021;26:173-7.
73. Zhang D, Zhou Z, Yang R, et al. Tristetraprolin, a potential safeguard against carcinoma: role in the tumor microenvironment. Front Oncol 2021;11:632189.
74. Selmi T, Martello A, Vignudelli T, et al. ZFP36 expression impairs glioblastoma cell lines viability and invasiveness by targeting multiple signal transduction pathways. Cell Cycle 2012;11:1977-87.
75. Selmi T, Alecci C, dell'Aquila M, et al. ZFP36 stabilizes RIP1 via degradation of XIAP and cIAP2 thereby promoting ripoptosome assembly. BMC Cancer 2015;15:357.
76. Han IW, Jang JY, Kwon W, et al. Ceruloplasmin as a prognostic marker in patients with bile duct cancer. Oncotarget 2017;8:29028-37.
77. Mukae Y, Ito H, Miyata Y, et al. Ceruloplasmin levels in cancer tissues and urine are significant biomarkers of pathological features and outcome in bladder cancer. Anticancer Res 2021;41:3815-23.
78. Senra Varela A, Lopez Saez J, Quintela Senra D. Serum ceruloplasmin as a diagnostic marker of cancer. Cancer Letters 1997;121:139-45.
79. Zhang J, Zhang Q, Zhang J, Wang Q. Expression of ACAP1 is associated with tumor immune infiltration and clinical outcome of ovarian cancer. DNA Cell Biol 2020;39:1545-57.
80. Wang Z, Tu L, Chen M, Tong S. Identification of a tumor microenvironment-related seven-gene signature for predicting prognosis in bladder cancer. BMC Cancer 2021;21:692.
81. Pan S, Zhan Y, Chen X, Wu B, Liu B. Bladder cancer exhibiting high immune infiltration shows the lowest response rate to immune checkpoint inhibitors. Front Oncol 2019;9:1101.
82. Gilder AS, Natali L, Van Dyk DM, et al. The Urokinase Receptor Induces a Mesenchymal Gene Expression Signature In Glioblastoma Cells And Promotes Tumor Cell Survival In Neurospheres. Sci Rep 2018;8:2982.
83. Zeng F, Li G, Liu X, et al. Plasminogen activator urokinase receptor implies immunosuppressive features and acts as an unfavorable prognostic biomarker in glioma. Oncologist 2021;26:e1460-9.
