Research
The Epigenetics & Computational RNA Biology Group conducts both fundamental and translational research on RNA regulation in cancer. Our work combines computational biology, long-read sequencing technologies, and experimental molecular biology to uncover how RNA processing and RNA modifications control tumor development and therapeutic response.
A central focus of our laboratory is the development and application of Nanopore direct RNA sequencing (dRNA-seq) together with AI-driven computational approaches. By integrating signal-level analysis, statistical modeling, and multi-omics data, we aim to bridge technology innovation with mechanistic discovery and clinical translation.
Our research is particularly centered on urologic oncology — including prostate cancer and other genitourinary malignancies — aligning with the clinical strengths of the Department of Urology at the First Affiliated Hospital of Xi’an Jiaotong University. Through close integration of computational analysis, experimental validation, and clinical datasets, we seek to define how epitranscriptomic regulation contributes to cancer initiation, progression, and therapeutic vulnerability.
We develop advanced computational algorithms, integrative analytical pipelines, and experimental workflows for systematic detection and characterization of diverse RNA modifications, including:
- 2′-O-methylation (Nm)
- N6-methyladenosine (m6A)
- pseudouridine (Ψ)
- ac4C
- A-to-I RNA editing
These tools enable quantitative profiling of RNA regulation from multiple high-throughput platforms such as Nanopore dRNA-seq, RNA-seq, Ribo-seq, and RIP-seq.
By combining computational modeling with functional perturbation experiments, we investigate how altered RNA modification programs reshape RNA stability, translation efficiency, and oncogenic signaling networks. Ultimately, our goal is to identify RNA-based biomarkers, reveal new therapeutic targets, and translate epitranscriptomic discoveries into clinically actionable strategies for precision oncology.
AI-enabled detection of RNA modifications
Many RNA modifications are low-abundance, context-dependent, and difficult to localize using conventional sequencing methods.
We develop computational frameworks that directly model Nanopore signal features to achieve high-resolution mapping of RNA modifications such as Nm, Ψ, and m6A. These methods allow quantitative comparison of modification dynamics across biological conditions, disease states, and treatment responses.
Regulation of RNA modifications in urological cancers
We investigate how RNA modification regulators become dysregulated in urologic cancers and how these alterations reshape post-transcriptional gene regulation. Our work focuses on understanding how RNA modification pathways influence RNA stability, translation, immune response, and tumor evolution, thereby contributing to malignant progression and therapy resistance.
Targeting RNA modification pathways for precision oncology
Building on mechanistic insights, we explore how RNA modification networks can be leveraged for clinical applications. This includes identifying diagnostic and prognostic biomarkers, uncovering druggable regulatory nodes, and designing therapeutic strategies targeting RNA modification enzymes and associated pathways.
Methods & Platforms
- Nanopore direct RNA sequencing (technology development and biological applications)
- Machine learning, deep learning, and statistical modeling
- Integrative multi-omics analysis (long-read sequencing, single-cell data, spatial transcriptomics)
- Functional validation using CRISPR perturbation, cell models, and mouse models