GlycoRNA-related cellular program scoring identifies Siglec- and myeloid-associated tumor microenvironments for glycoRNA validation
6 affiliations
- 1Department of Cardiology, The Affiliated Hospital of Southwest Medical University
- 2Department of Orthopedics, Zigong Fourth People’s Hospital
- 3Key Laboratory of Medical Electrophysiology, Ministry of Education, Institute of Cardiovascular Research
- 4Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University
- 5Basic Medicine Research Innovation Center for Cardiometabolic Diseases, Ministry of Education, Southwest Medical University
- 6Nucleic Acid Medicine of Luzhou Key Laboratory
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.
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BibTeX
@article{kong2026glycornarelated,
title = {{GlycoRNA-related cellular program scoring identifies Siglec- and myeloid-associated tumor microenvironments for glycoRNA validation}},
author = {Mowei Kong and Xiangfeng Guan and Yuling Jing and Zheng Xie and Xingwei Zhao and Shengyu Huang and Yang Yu and Chunxiang Zhang},
journal = {Frontiers in Immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1928501},
url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1928501/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - GlycoRNA-related cellular program scoring identifies Siglec- and myeloid-associated tumor microenvironments for glycoRNA validation
AU - Mowei Kong
AU - Xiangfeng Guan
AU - Yuling Jing
AU - Zheng Xie
AU - Xingwei Zhao
AU - Shengyu Huang
AU - Yang Yu
AU - Chunxiang Zhang
PY - 2026
DA - 2026/10/13
JO - Frontiers in Immunology
DO - 10.3389/fimmu.2026.1928501
UR - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1928501/full
AB - 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.
SN - 1664-3224
ER -