---
title: "GAA as a Prognostic Biomarker and Therapeutic Target in Angioimmunoblastic T‑cell Lymphoma — Full"
id: "frontiers-in-immunology-16-multi-omics-analysis-identifies-gaa-as-an-independent-poor-prognostic-biomarker"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-16-multi-omics-analysis-identifies-gaa-as-an-independent-poor-prognostic-biomarker"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1862168"
published_at: "2026-08-11T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# GAA as a Prognostic Biomarker and Therapeutic Target in Angioimmunoblastic T‑cell Lymphoma — Full
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-16-multi-omics-analysis-identifies-gaa-as-an-independent-poor-prognostic-biomarker
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1862168)
- **Published At:** 2026-08-11T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The provided source page did not include the article body or detailed study data; only site navigation and journal information were present. - The article title identifies **GAA** and **angioimmunoblastic T‑cell lymphoma** as the topic and implies a multi‑omics analysis reporting GAA as a poor prognostic biomarker and candidate therapeutic target. - No methods, cohort details, results, statistical analyses, figures, tables, or author and institutional information were available in the supplied content. - The only verifiable metadata are the journal name (Frontiers in Immunology) and the article URL; the full manuscript content must be accessed at the publisher site to review study details. - Because the source text lacks the study content, no clinical conclusions, numeric outcomes, or recommendations can be summarized or restated from the article. - To interpret the study clinically or consider implications for practice, clinicians should retrieve the complete article and supplementary materials from the journal page or contact the authors/publisher for the full dataset.
## Clinical Analysis & Structured Key Points
Frontiers | Multi-omics analysis identifies GAA as an independent poor prognostic biomarker and candidate therapeutic target in angioimmunoblastic T-cell lymphoma ORIGINAL RESEARCH article Front. Immunol. , 11 August 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1862168 Published in Frontiers in Immunology Cancer Immunity and Immunotherapy 7 impact factor 11.3 citescore Part of a Research Topic The Insights of Multi-Omics into the Microenvironment After Tumor Metastasis: A Paradigm Shift in Molecular Targeting Modeling and Immunotherapy for Advanced Cancer Patients - Vol II 30k views 15 articles Editor & Reviewers Edited by X X Xinhua Xiao Reviewed by J P Jingjing Pu T H Tingting Huang Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Table 1 Baseline characteristic of 39 AITL patients. View in article Table 2 Univariate and multivariate analysis of prognostic factors for predicting the OS of AITL. View in article ORIGINAL RESEARCH article Front. Immunol. , 11 August 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1862168 Multi-omics analysis identifies GAA as an independent poor prognostic biomarker and candidate therapeutic target in angioimmunoblastic T-cell lymphoma Y L Yanfei Liu 1,2 † Y S Yunfei Shi 3 † Y Z Yang Zhao 4 † H W Haojie Wang 1 L M Lan Mi 1 M W Meng Wu 1 Y S Yuqin Song 1 J Z Jun Zhu 1 Y X Yan Xie 1 * 1. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China 2. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Comprehensive Clinical Trial Ward, Peking University Cancer Hospital & Institute, Beijing, China 3. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China 4. Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China See more Article metrics View details Abstract Background: Angioimmunoblastic T-cell lymphoma (AITL) is an aggressive subtype of peripheral T-cell lymphoma with poor clinical outcomes and limited therapeutic options. The contribution of metabolic reprogramming and immune microenvironmental alterations to AITL progression remains insufficiently defined. Methods: We conducted integrative transcriptomic and proteomic analyses of AITL samples compared with reactive lymphoid hyperplasia to identify molecules associated with treatment response and prognosis. Immune infiltration and pathway enrichment analyses were performed, and findings were validated in independent GEO cohorts (GSE19069 and GSE58445). Key protein expression was confirmed by immunohistochemistry and multiplex immunofluorescence. Results: Differential expression analyses revealed that lysosomal alpha-glucosidase (GAA), a key enzyme involved in glycogen metabolism, was significantly upregulated at both the RNA and protein levels in patients who failed to respond to standard chemotherapy. Elevated GAA expression was associated with inferior overall survival in multivariate analysis. Immunohistochemistry staining and multiplex immunofluorescence further confirmed the spatial expression pattern of GAA in AITL tissues. Immune deconvolution and pathway enrichment analyses suggested that GAA-high tumors exhibited increased CD8 + T-cell infiltration accompanied by transcriptional features of T-cell exhaustion, as well as transcriptionally inferred metabolic alterations characterized by enhanced glycolysis-related signatures and reduced oxidative phosphorylation-related signatures. These findings were further validated in an independent AITL cohort from the GEO database. Conclusions: Our study identifies GAA as a candidate biomarker associated with adverse clinical outcomes, immune microenvironmental features, and metabolic pathway alterations in AITL, highlighting glycogen metabolism as a previously underexplored biological axis and a potential therapeutic vulnerability warranting further functional investigation. Graphical Abstract 1 Background Angioimmunoblastic T-cell lymphoma (AITL) is a rare and aggressive subtype of peripheral T-cell lymphoma that is clinically characterized by systemic symptoms, including fever, generalized lymphadenopathy, and autoimmune manifestations. Despite advances in the understanding of its pathogenesis, AITL remains associated with poor clinical outcomes, with most patients diagnosed at advanced stages and reported 5-year overall survival (OS) rates ranging from 32% to 44% ( 1 – 4 ). Current first-line treatment strategies are largely based on conventional chemotherapy regimens such as CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone), which have shown limited efficacy in improving long-term survival, particularly in relapsed or refractory disease ( 5 , 6 ). Although several novel agents, including histone deacetylase inhibitors, EZH2 inhibitors, and CD30-targeted monoclonal antibodies, have demonstrated clinical activity in early-phase trials, their broader application remains limited by patient heterogeneity, acquired drug resistance, and the lack of reliable predictive biomarkers ( 7 ). Accordingly, there is a continued need to identify molecular determinants that underlie disease progression and therapeutic resistance in AITL. Previous studies have extensively characterized the genomic and transcriptomic landscape of AITL. Recurrent mutations in genes such as RHOA, TET2, DNMT3A, and IDH2 have provided important insights into disease pathogenesis and contributed to the molecular classification of AITL ( 8 , 9 ). Gene expression profiling has further shown that B-cell–associated signatures are linked to favorable clinical outcomes, whereas signatures related to monocytic differentiation, CD8 + T-cell cytotoxicity, and activation of the p53 pathway are associated with inferior survival ( 10 ). While these findings have substantially advanced the understanding of AITL biology, their translation into clinically actionable targets has been limited. In addition, the interaction between malignant T cells and the immune microenvironment remains incompletely understood, particularly with respect to the role of CD8 + T cells, which appear to exert context-dependent and potentially dysfunctional effects in AITL. Recent evidence suggests that metabolic reprogramming may influence both tumor behavior and immune cell function within the tumor microenvironment ( 11 , 12 ); however, the metabolic features of AITL and their clinical relevance have not been systematically investigated. In the present study, we performed integrated transcriptomic and proteomic profiling of AITL specimens with the aim of identifying disease-associated molecules with prognostic relevance. Using reactive lymphoid hyperplasia (RLH) as a control, we identified differentially expressed proteins associated with treatment response and found that lysosomal alpha-glucosidase (GAA), a key enzyme involved in glycogen metabolism, was consistently overexpressed at both the RNA and protein levels in patients who did not respond to standard chemotherapy. Elevated GAA expression was found to be associated with inferior overall survival in multivariate analysis. We additionally examined the association between GAA expression, immune cell infiltration, and related biological pathways, and validated these findings in an independent AITL cohort from the Gene Expression Omnibus (GEO) database. Together, these results indicate that GAA expression correlates with metabolic pathway signatures, immune microenvironmental features, and clinical outcome in AITL, supporting its role as a candidate prognostic biomarker and putative therapeutic vulnerability warranting further functional investigation. 2 Materials and methods 2.1 Patient samples We retrospectively identified 39 newly diagnosed patients with AITL who had available tumor resection specimens and were treated in the Department of Lymphoma at Peking University Cancer Hospital between August 2014 and August 2022. Patients were included if they met the following criteria (1): a pathological diagnosis of AITL confirmed by experienced hematopathologists at our institution; (2) availability of archived tumor resection specimens; and (3) complete baseline clinical data. Patients who were lost to follow-up were excluded. In addition, five cases of RLH diagnosed during the same period were included as controls. For independent validation, transcriptomic data from two publicly available AITL cohorts (GSE19069 and GSE58445; n = 52) were retrieved from the GEO database ( https://www.ncbi.nlm.nih.gov/gds/ ). 2.2 RNA sample preparation and sequencing Total RNA was extracted from 39 formalin-fixed paraffin-embedded (FFPE) tumor samples using the miRNeasy FFPE Kit (Qiagen), according to the manufacturer’s protocol. RNA quality and concentration were assessed using an Agilent Bioanalyzer 2100 and a Qubit 3.0 Fluorometer, respectively. Ribosomal RNA was depleted using the KAPA Stranded RNA-seq Kit with RiboErase (HMR), and sequencing libraries were constructed following the manufacturer’s instructions. Libraries were sequenced in paired-end mode (150 bp) on an Illumina HiSeq 4000 platform. Raw sequencing reads were subjected to quality control using Trimmomatic (v0.33) and subsequently aligned to the reference genome using STAR (v2.5.3a). Gene-level expression quantification was performed with RSEM (v1.3.0). 2.3 Protein sample preparation and mass spectrometry-based proteomic analysis Deparaffinized FFPE tissue sections were processed for protein extraction using 300 mM Tris-HCl containing 50% acetonitrile, followed by sequential water-bath sonication and heating. Proteins were reduced, alkylated, and digested with trypsin at an enzyme-to-protein ratio of 1:10 at 37 °C for 20 h. Peptides were acidified, centrifuged, and desalted using SDB-RPS StageTips. After washing and elution, peptides were vacuum-dried and stored at −80 °C until further analysis. Peptide separation was performed on a homemade C18 column (30 cm length, 1.9 μm particle size) using a 135-min gradient at a flow rate of 600 nL/min on an EASY-nLC 1200 system coupled to an Orbitrap Fusion Lumos mass spectrometer. Data were acquired in data-independent acquisition (DIA) mode, with MS1 and MS2 resolutions set to 120,000 and 30,000, respectively. DIA raw files were analyzed using Spectronaut (v14.9.201124) with the directDIA workflow. Digestion was specified as trypsin/P, dynamic mass tolerance calibration was applied, and retention times were aligned using the Biognosys iRT kit. A mutated decoy strategy was used to control false discovery rates. Protein and peptide identification and quantification were performed using default parameters with minor adjustments. 2.4 Bioinformatic analysis The overall proportion of missing data was 13.8%, and missing values were imputed using the minimum observed value for each protein. Differential gene and protein expression analyses were performed using the limma package in R. TPM expression data were log2-transformed prior to analysis, and the ORR group was used as the reference group for all comparisons. Genes or proteins with |log 2 (fold change)| > 0.58 and a P-value 1 6(15.4%) B symptom Yes 18(46.2%) No 20(51.3%) Extranodal, n (%) >1 10(25.6%) 0.58 and nominal P 0.58 and nominal P < 0.1; Figure 1D ; Supplementary Table 4 ). To further identify proteins associated with treatment response, differential analysis based on ORR was performed within the AITL cohort, resulting in 138 response-related proteins (73 up-regulated and 65 down-regulated; DEP2; Figure 1E ; Supplementary Table 5 ). To identify molecules consistently associated with treatment response across transcriptomic and proteomic layers, three datasets (DEG1, DEG2, and DEP2) were integrated. Only molecules showing concordant directional changes across datasets were retained. Venn diagram analysis revealed seven overlapping candidates ( Figure 1F ). Among these, GAA and APOL3 were up-regulated in non-responders
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