GlycoRNA-related cellular program scoring identifies Siglec- and myeloid-associated tumor microenvironments for glycoRNA validation

Our plain-English summary of GlycoRNA-related cellular program scoring identifies Siglec- and myeloid-associated tumor microenvironments for glycoRNA validation, by M. Kong, X. Guan, Y. Jing, et al (Frontiers in Immunology, 2026).

The study

• Authors: M. Kong, X. Guan, Y. Jing, Z. Xie, X. Zhao, S. Huang, Y. Yu, C. Zhang
• Journal: Frontiers in Immunology
• Published: 2026;17:1928501
• Read it: PubMed · Publisher · Free full text

Abstract

Background: Glycosylated RNAs (glycoRNAs) represent a recently recognized connection between RNA biology and cell-surface glycobiology. However, whether glycoRNA-related processes are relevant to human tumors remains unclear, partly because direct glycoRNA measurements are not yet available across large cancer cohorts. We therefore used transcriptomic data to identify tumor settings in which glycoRNA-related programs may be most suitable for further experimental validation.

Methods: We curated a glycoRNA-focused literature evidence base and manually reviewed the top extracted relations to define gene modules related to RNA identity, N-glycan and sialylation pathways, RNA trafficking, and lectin/Siglec recognition. The glycoRNA-related cellular program score is an expression-based composite score representing coordinated glycoRNA-related cellular programs, including RNA processing, glycan biosynthesis, trafficking, and lectin/Siglec recognition. It is designed to prioritize tumor contexts for experimental glycoRNA validation. The score was applied to 9,186 TCGA tumors from 32 cancer types and analyzed together with clinical variables, leukocyte fraction, immune signatures, Siglec/checkpoint/myeloid-axis scores, and non-overlapping sensitivity models. Glioblastoma single-cell and spatial transcriptomic datasets were used for additional context-level validation.

Results: Higher glycoRNA-related cellular program scores were observed in several tumor types, including stomach adenocarcinoma, glioblastoma, lung adenocarcinoma, esophageal carcinoma, mesothelioma, pancreatic adenocarcinoma, breast cancer, and lung squamous carcinoma. Survival analyses did not show robust FDR-significant associations after adjustment for age, sex, stage, and tumor type, suggesting that the score should not be interpreted as a general prognostic biomarker. In sensitivity analyses that removed immune and Siglec-related genes from the predictor, the score remained associated with Siglec-decoder, myeloid, checkpoint, and integrated niche features after adjustment for leukocyte fraction and cancer-type effects. Within-cancer meta-analysis supported these associations across cancer types, although effect sizes varied. In glioblastoma, higher scores were found in tumor, myeloid, mesenchymal, and niche-associated compartments in single-cell and spatial analyses.

Conclusions: This study presents an expression-based map of glycoRNA-related cellular program across human cancers. The results suggest that glycoRNA-related transcriptional programs tend to occur in immune- and myeloid-enriched tumor microenvironments, particularly those involving Siglec-associated features. These findings prioritize tumor contexts for biochemical, imaging, spatial, and perturbation-based glycoRNA validation.

Copyright © 2026 Kong, Guan, Jing, Xie, Zhao, Huang, Yu and Zhang.

In plain English

This was a computer-based analysis of publicly available tumor gene-expression data. It did not study asbestos or any other exposure. The authors used a literature review to build a 74-gene, six-module score linked to glycosylated RNAs (glycoRNAs), RNA molecules carrying sugar chains. They stressed that the score does not detect glycoRNA itself. They applied it to 9,186 score records, representing 9,100 distinct tumor samples, from 32 cancer types in The Cancer Genome Atlas (TCGA). Mesothelioma was among the highest-scoring cancer types, along with stomach, lung, esophageal, pancreatic and breast cancers and glioblastoma. In survival models adjusted for age, sex and stage (and, in the all-cancer model, tumor type), none of 19 fitted models passed the multiple-testing threshold (FDR less than 0.1). A version of the score without Siglec-related genes stayed positively associated with Siglec, myeloid (immune cell) and checkpoint gene scores after adjustment for immune-cell content and cancer type. The authors also analyzed glioblastoma single-cell and spatial data. They concluded that their model generates hypotheses and is not direct evidence that glycoRNA plays a causal role.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Computational (bioinformatic) analysis of existing public gene-expression datasets, using an expression-based score built from a literature review; no laboratory experiments
• Who was studied: 9,186 score records representing 9,100 distinct tumor samples from 32 cancer types in The Cancer Genome Atlas (TCGA). One of these cancer types was mesothelioma; the article does not specify pleural or peritoneal. The study also used a glioblastoma single-cell dataset of 123,236 cells and other glioblastoma single-cell and spatial datasets.
• Exposure: Not applicable: no asbestos, fiber or occupational exposure was examined. The variable studied was a gene-expression score.
• Main results:
  • The highest-scoring TCGA cancer types included stomach adenocarcinoma, glioblastoma, lung adenocarcinoma, esophageal carcinoma, mesothelioma, pancreatic adenocarcinoma, breast cancer, and lung squamous carcinoma
  • Multivariable Cox survival models used score, age, sex and stage, and the pan-cancer model also included tumor type. These produced 19 fitted models, and none passed FDR less than 0.1.
  • Across all 9,186 TCGA records, the score with lectin/Siglec genes removed correlated with Siglec-decoder, myeloid, checkpoint and integrated niche outcomes (Spearman rho 0.398, 0.372, 0.248 and 0.541)
  • After adjustment for leukocyte fraction and cancer type (8,971 records, 30 cancer types), the lectin/Siglec-module-removed score was positively associated with Siglec-decoder score (beta 0.234, 95% CI 0.218 to 0.251), myeloid score (beta 0.266, 95% CI 0.249 to 0.282) and checkpoint score (beta 0.126, 95% CI 0.112 to 0.140)
  • Within individual cancer types, the Siglec-decoder association was positive at FDR less than 0.05 in 29 of 32 cancer types. Effect sizes varied between cancer types (heterogeneity I2 82.9% to 89.1%).
  • A label-permutation analysis gave an empirical FDR of 1.0 for the four immune-axis outcomes. The authors did not discuss this further in the text.
  • In glioblastoma single-cell data, scores were highest in tumor cells (mean 2.33), fibroblasts (1.78), monocyte-derived macrophages (1.66), microglia-derived macrophages (1.34) and vascular/endothelial cells (1.16)
  • In an independent glioblastoma single-cell set (16,201 cells), the score correlated with mesenchymal and niche programs (rho 0.546 and 0.598). Its correlations with myeloid, Siglec and checkpoint scores were small (rho 0.065, 0.069 and 0.041).
• The authors' conclusion: The authors concluded that their model is hypothesis-generating and meant to rank tumor contexts for future laboratory validation. In their words, it is not direct evidence that glycoRNA has a causal role or regulates immune activity in these tumors.
• Limitations the authors state:
  • The score is inferential and does not directly detect glycosylated RNA molecules
  • The Cox survival associations did not survive FDR correction and are exploratory
  • Bulk tissue scoring cannot show which cell types express each module or whether the same cells express them
  • The glioblastoma validation is expression-based and does not visualize glycoRNA
  • No direct perturbation experiments or biochemical confirmation of glycoRNA were performed

Topics

Medical terms in this study, explained in our Lexicon: Tumor Microenvironment, Adenocarcinoma, Lung Adenocarcinoma, Carcinoma, Mesothelioma, Biomarker, Meta-Analysis.

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