APOE expression is associated with molecular class, context-dependent survival and myeloid immune features in lower-grade glioma

Abstract

Background: The APOE gene encodes ApoE, a lipid-transport protein implicated in neuroinflammatory processes. Whether APOE expression independently predicts outcome in lower-grade glioma (LGG), after accounting for IDH mutation and 1p/19q codeletion, remains unclear.

Methods: We analysed The Cancer Genome Atlas (TCGA) pan-cancer and LGG transcriptomic and clinical data. In LGG, APOE was modelled continuously in Cox regression adjusted for age, histological grade and IDH/1p/19q molecular class; molecular-class-stratified and interaction analyses were performed. Results were evaluated in the Chinese Glioma Genome Atlas (CGGA)_693 and CGGA_325 cohorts. Immune deconvolution, myeloid/lipid programme scores and author-annotated GSE197543 single-cell data were used to define the immune and cellular context of APOE.

Results: Higher APOE expression was associated with favourable survival in univariable TCGA-LGG analyses, but not after adjustment for molecular class (overall survival hazard ratio per 1-SD increase, 1.021; 95% confidence interval, 0.830–1.256; p = 0.846). The APOE–survival association differed across molecular classes (global interaction p = 1.92 × 10⁻⁴), including an adverse association in IDH-wild-type tumours. External results were heterogeneous: the unadjusted association in CGGA_693 attenuated after adjustment and was not consistently reproduced in CGGA_325. APOE correlated positively with tumour-associated macrophage/macrophage transcriptional programmes in TCGA and both CGGA cohorts. Single-cell data localised APOE expression primarily to microglial and macrophage populations.

Conclusion: The apparent favourable association between APOE and LGG survival is molecular-context dependent and not consistently independent across cohorts. APOE may primarily mark a myeloid immune–metabolic context rather than an independently protective prognostic determinant.

Keywords

APOE / CGGA / lower-grade glioma / molecular subtype / prognosis / single-cell RNA sequencing / tumour-associated macrophage

References

[1] Raulin A-C, Doss SV, Trottier ZA, Ikezu TC, Bu G, Liu C-C. ApoE in Alzheimer's disease: pathophysiology and therapeutic strategies. Mol Neurodegener. 2022; 17(1): 72.

[2] Blumenfeld J, Yip O, Kim MJ, Huang Y. Cell type-specific roles of APOE4 in Alzheimer disease. Nat Rev Neurosci. 2024; 25(2): 91-110. doi: https://doi.org/10.1038/s41583-023-00776-9

[3] van den Bent MJ, Geurts M, French PJ, et al. Primary brain tumours in adults. Lancet. 2023; 402(10412): 1564-1579.

[4] Kloosterman DJ, Erbani J, Boon M, et al. Macrophage-mediated myelin recycling fuels brain cancer malignancy. Cell. 2024; 187(19): 5336-5356. doi: https://doi.org/10.1016/j.cell.2024.07.030

[5] Wang S, Yan W, Kong L, et al. Oncolytic viruses engineered to enforce cholesterol efflux restore tumor-associated macrophage phagocytosis and anti-tumor immunity in glioblastoma. Nat Commun. 2023; 14(1): 4367.

[6] Zhao Z, Zhang K-N, Wang Q, et al. Chinese glioma genome atlas (CGGA): a comprehensive resource with functional genomic data from Chinese glioma patients. Genomics Proteomics Bioinformatics. 2021; 19(1): 1-12. doi: https://doi.org/10.1016/j.gpb.2020.10.005

[7] Schmassmann P, Roux J, Dettling S, et al. Single-cell characterization of human GBM reveals regional differences in tumor-infiltrating leukocyte activation. Elife. 2023; 12: RP92678. doi: https://doi.org/10.7554/eLife.92678

[8] Lopez-Ayala P, Riley RD, Collins GS, Zimmermann T. Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide. BMJ. 2025; 390: e082440.

[9] Efthimiou O, Seo M, Chalkou K, Debray T, Egger M, Salanti G. Developing clinical prediction models: a step-by-step guide. BMJ. 2024; 386: e078276.

[10] Sauerbrei W, Haeussler T, Balmford J, Huebner M. Structured reporting to improve transparency of analyses in prognostic marker studies. BMC Med. 2022; 20(1): 184.

[11] Weller M, Wen PY, Chang SM, et al. Glioma. Nat Rev Dis Primers. 2024; 10(1): 33.

[12] Garmire LX, Li Y, Huang Q, et al. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods. 2024; 21(3): 391-400.

[13] White BS, de Reyniès A, Newman AM, et al. Community assessment of methods to deconvolve cellular composition from bulk gene expression. Nat Commun. 2024; 15(1): 7362.

[14] Rosati D, Palmieri M, Brunelli G, et al. Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: a review. Comput Struct Biotechnol J. 2024; 23: 1154-1168.

[15] Zhao K, Rhee SY. Interpreting omics data with pathway enrichment analysis. Trends Genet. 2023; 39(4): 308-319.

[16] Rahnenführer J, De Bin R, Benner A, et al. Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges. BMC Med. 2023; 21(1): 182.

[17] Riley RD, Archer L, Snell KIE, et al. Evaluation of clinical prediction models (part 2): how to undertake an external validation study. BMJ. 2024; 384: e074820.

[18] Zhang Z, Lai G, Sun L. Basement-membrane-related gene signature predicts prognosis in WHO grade II/III gliomas. Genes. 2022; 13(10): 1810.

[19] Wischnewski V, Maas RR, Aruffo PG, et al. Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation. Nat Cancer. 2023; 4(6): 908-924.

[20] Miller TE, El Farran CA, Couturier CP, et al. Programs, origins and immunomodulatory functions of myeloid cells in glioma. Nature. 2025; 640(8060): 1072-1082.

[21] Lai G, Xie B, Zhang C, et al. Comprehensive analysis of immune subtype characterization on identification of potential cells and drugs to predict response to immune checkpoint inhibitors for hepatocellular carcinoma. Genes Dis. 2024; 12(3): 101471.

[22] Tran KA, Addala V, Johnston RL, et al. Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures. Nat Commun. 2023; 14(1): 5758.

[23] Blériot C, Dunsmore G, Alonso-Curbelo D, Ginhoux F. A temporal perspective for tumor-associated macrophage identities and functions. Cancer Cell. 2024; 42(5): 747-758.

[24] Dai Y, Guo S, Pan Y, et al. A guide to transcriptomic deconvolution in cancer. Nat Rev Cancer. 2025; 26(2): 84-103. doi: https://doi.org/10.1038/s41568-025-00886-9

[25] Wu C, Xie X, Yang X, Du M, Lin H, Huang J. Applications of gene pair methods in clinical research: advancing precision medicine. Mol Biomed. 2025; 6(1): 22.

[26] Lai G, Li K, Deng J, Liu H, Xie B, Zhong X. Identification and validation of a gene signature for lower-grade gliomas based on pyroptosis-related genes to predict survival and response to immune checkpoint inhibitors. J Healthc Eng. 2022; 2022: 8704127.

[27] Xie B, Li K, Zhang H, Lai G, Li D, Zhong X. Identification and validation of an immune-related gene pairs signature for three urologic cancers. Aging. 2022; 14(3): 1429-1447.

[28] Zhang C, Shi D, Lai G, et al. A transcriptome-wide association study integrating multi-omics bioinformatics and Mendelian randomization reveals the prognostic value of ADAMDEC1 in colon cancer. Arch Toxicol. 2024; 99(2): 645-665.

[29] Zhang C, Deng J, Li K, et al. Causal association of monocytes with chronic kidney disease and the mediation role of frailty: a study integrating large-scale two-sample Mendelian randomization and single-cell analysis. Arch Gerontol Geriatr. 2024; 123: 105435.

Rights & permissions

2026 The Author(s). Clinical and Translational Discovery published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.