# Functional and clinical validation of tsRNA-defined molecular subtypes guides precision therapy in gastric cancer

## TL;DR

This study identifies new gastric cancer subtypes based on tsRNAs and shows how they can guide personalized treatment strategies.

## Contribution

The study introduces tsRNA-based subtyping and a prognostic model (GCtsRNAscore) for precision therapy in gastric cancer.

## Key findings

- Three tsRNA-defined subtypes with distinct stromal activity, tumor microenvironment, and clinical outcomes were identified.
- The GCtsRNAscore model effectively stratifies patients into high- and low-risk groups with treatment implications.
- tsRNA-Asp-3-0024 promotes cancer cell proliferation and inhibits apoptosis, suggesting it as a potential therapeutic target.

## Abstract

Gastric cancer (GC) is a highly heterogeneous malignancy with poor prognosis, underscoring the urgent need for reliable biomarkers to guide precise stratification and therapy. Transfer RNA-derived small RNAs (tsRNAs) have emerged as potential key regulators in cancer, yet their systematic role in defining GC subtypes remains unexplored.

We profiled tsRNA expression in GC using transcriptomic data from TCGA and GEO databases. Unsupervised consensus clustering identified tsRNA-based subtypes. A prognostic model was constructed using machine learning algorithms and validated across multiple cohorts. The functional role of a key tsRNA, tsRNA-Asp-3-0024, was investigated through Pandora-seq, qRT-PCR, and in vitro and organoid-based assays.

Three distinct tsRNA-mediated subtypes (Stromal_H, Stromal_L, Stromal_M) were identified, exhibiting significant differences in stromal activity, tumor microenvironment, and clinical outcomes. The Stromal_H subtype demonstrated the poorest prognosis, characterized by an immunosuppressive microenvironment and dysregulated DNA repair pathways. A random survival forest (RSF)-based prognostic signature (GCtsRNAscore) effectively stratified patients into high- and low-risk groups, with high-risk patients showing increased sensitivity to targeted therapies (axitinib, bexarotene, dasatinib) and low-risk patients benefiting more from immunotherapy. Furthermore, tsRNA-Asp-3-0024 was significantly upregulated in GC tissues and cell lines, where it promoted proliferation and inhibited apoptosis.

Our study establishes tsRNAs as powerful biomarkers for molecular subtyping and prognostic prediction in GC. The tsRNA-defined subtypes and GCtsRNAscore model provide a novel framework for personalized treatment strategies. The functional characterization of tsRNA-Asp-3-0024 highlights its potential as both a therapeutic target and a prognostic indicator, paving the way for tsRNA-based precision medicine in GC.

Diagram illustrating the regulation of the gastric cancer immune microenvironment by tsRNA. The left side shows a transition from good prognosis with health icons to poor prognosis with diseased icons. The bottom left displays elements like tsRNA subtypes, genomic alterations, tsRNA network, machine learning, therapy analysis, Pandora sequencing, single cell analysis, cell, and organoid models. The right section details tsRNA processing, labeled sequences, and models focusing on apoptosis, migration, and proliferation, with pathways and interactions highlighted.