GlycoRNA: A new player in cellular communication

Abstract

The discovery of glycosylated RNA molecules, known as glycoRNAs, introduces a novel dimension to cellular biology. This commentary explores the transformative findings surrounding glycoRNAs, emphasizing their unique roles in cancer progression and the therapeutic opportunities they present. GlycoRNAs, through interactions with lectins and immune receptors, may contribute to tumor immune evasion. Moreover, the therapeutic potential of this emerging knowledge includes interventions targeting glycoRNA synthesis and modulation of associated signaling pathways. By highlighting these critical insights, this commentary aims to encourage the development of innovative strategies that could improve cancer prognosis and treatment.

Keywords: Cancer biology; GlycoRNA; Glycosylation; Immune evasion; Lectin interactions; RNA modifications; Therapeutic targets.

Figure 1. Mechanism of glycoRNA biosynthesis and surface localization. GlycoRNAs undergo a biosynthetic pathway that begins with the modification of specific RNA molecules by the addition of acp3U (3-(3-amino-3-carboxypropyl)uridine), facilitated by the enzyme DTWD2. Following this modification, N-linked glycans are attached to the acp3U site, creating glycoRNAs. These glycoRNAs are subsequently transported through cellular compartments and ultimately displayed on the cell surface.

Figure 2. GlycoRNA interaction with SIGLECs (sialic acid-binding immunoglobulin-like lectins) on suppressive immune cells. GlycoRNA displayed on the surface of cancer cells interacts with SIGLEC receptors on suppressive immune cells. By binding to SIGLECs, glycoRNA may trigger inhibitory signaling pathways that contribute to immune suppression within the tumor microenvironment. This interaction enables cancer cells to evade immune detection and promotes tumor growth by dampening the immune response.

Figure 3. Expression levels of glycoRNA-related genes across various cancer types. (A) and (B) display gene expression data for GALNT14 and ST6GAL1, respectively, analyzed using TCGA (The Cancer Genome Atlas) pan-cancer datasets. Cancer types in red indicate gene overexpression, while those in green represent gene underexpression. ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.