Rongxin Zhang

All Publications

• Multilevel ensemble for genome-wide prediction of cell-specific G-quadruplexes utilizing transformed sequences and enhanced chromatin accessibility with G4Beacon2. Genome research Tao, T., Zhang, R., Shu, H., Ma, Y., Zhang, Z., Tu, J., Sun, X. 2026

  Abstract

  G-quadruplexes (G4) are prevalent non-B DNA structures playing crucial biological roles in cells. Although experimental technologies for G4 identification in vitro and in vivo are advancing, computational prediction methods are increasingly preferred for their efficiency, convenience, and cost effectiveness. However, existing tools primarily perform in vitro G4 predictions that lack cell-specific information and are often non-genome-wide, with high-performance genome-wide in vivo cell-specific G4 prediction models still lacking. Here, we present G4Beacon2, a genome-wide cell-specific G4 prediction model based on multilevel ensemble learning. G4Beacon2 leverages DNABERT2 to capture semantic information from DNA sequences, normalizes chromatin accessibility data using Z-score, and establishes a three-level ensemble framework to achieve accurate in vivo G4 predictions across the genome. Across the evaluated intra-cell-line and cross-cell-line tests, G4Beacon2 shows consistently favorable performance. Exploratory analyses further indicate that mouse-derived training information may contribute to human prediction under the currently available data sets. Moreover, the fusion model, integrating high-quality multisource data, improves the stability and performance of human cell-specific G4 prediction. In summary, G4Beacon2 presents a novel solution for human genome-wide in vivo G4 prediction by integrating multisource data, offering a user-friendly and advanced prediction tool for researchers.

  View details for DOI 10.1101/gr.280606.125

  View details for PubMedID 42772960

• TNEAtlas: A Pan-cancer Database to Identify and Characterize Transcribed Non-coding Elements. Genomics, proteomics & bioinformatics Zhu, W., Zhang, R., Sun, X. 2026

  Abstract

  Advances in precision oncology have underscored the need to move beyond regulatory frameworks centered on protein-coding regions and better understand regulatory mechanisms within the non-coding genome. To systematically characterize transcribed non-coding elements (TNEs) in cancer, we developed an automated computational framework that integrates 130 RNA-seq datasets from 26 cancer types and 16 human tissues and identifies more than 2 million intergenic TNEs. In parallel, we established the first pan-cancer TNE database, TNEAtlas, which annotates TNEs with epigenetic signatures and functional features. Our analyses revealed that TNEs act as molecular switches that drive tumor evolution through conserved regulatory functions, tissue-specific transcription factor recruitment, and epigenetic modification crosstalk. We also demonstrated that these TNEs exhibit structural motif preferences, especially G-quadruplexes, and tumor heterogeneity patterns consistent with cancer subtype classifications. Our database implemented an interactive platform comprising dynamic visualization tools, integrated analysis modules, and structured data resources to enable the exploration of TNE regulatory networks across multi-omics data. Our database provides a systematic framework for decoding non-coding genome regulation in carcinogenesis by combining transcriptional activity, chromatin architecture, tumor microenvironment interactions, and comprehensive pharmacological data. Overall, our database not only advances precision medicine by facilitating the identification of functional TNEs but also offers comprehensive analysis frameworks of the non-coding cancer genome, providing the scientific community with an open-access platform that bridges fragmented TNE studies with systematic exploration of genomic machinery and remains scalable for future discoveries in the non-coding cancer genome. TNEAtlas is publicly accessible at https://www.seubioinfo.cn/tneatlas.

  View details for DOI 10.1093/gpbjnl/qzag061

  View details for PubMedID 42421226

• Archaeal G-quadruplexes: a novel model for understanding unusual DNA/RNA structures across the tree of life NUCLEIC ACIDS RESEARCH Aktary, Z., Sorg, K., Cucchiarini, A., Vesco, G., Noury, D., Zhang, R., Jourdain, T., Verga, D., Mahou, P., Olivier, N., Bohalova, N., Porubiakova, O., Brazda, V., Bouvier, M., Kwapisz, M., Clouet-d'Orval, B., Allers, T., Lestini, R., Mergny, J., Guittat, L. 2026; 54 (4)

  Abstract

  Archaea, a domain of microorganisms found in diverse environments, including the human microbiome, represent the closest known prokaryotic relatives of eukaryotes. This phylogenetic proximity positions them as a relevant model for investigating the evolutionary origins of nucleic acid secondary structures such as G-quadruplexes (G4s) which play regulatory roles in transcription and replication. Although G4s have been extensively studied in eukaryotes, their presence and function in archaea remain poorly characterized. In this study, a genome-wide analysis of the halophilic archaeon Haloferax volcanii identified over 5800 potential G4-forming sequences. Biophysical validation confirmed that many of these sequences adopt stable G4 conformations in vitro. Using G4-specific detection tools and super-resolution microscopy, G4 structures were visualized in vivo in both DNA and RNA across multiple growth phases. Comparable findings were observed in the thermophilic archaeon Thermococcus barophilus. Functional analysis using helicase-deficient H. volcanii strains further identified candidate enzymes involved in G4 resolution. These results establish H. volcanii as a tractable archaeal model for G4 biology.

  View details for DOI 10.1093/nar/gkag067

  View details for Web of Science ID 001680170800001

  View details for PubMedID 41641698

  View details for PubMedCentralID PMC12873603

• G-quadruplex structures as modulators of alternative promoter usage NAR GENOMICS AND BIOINFORMATICS Zhang, R., Mergny, J. 2025; 7 (4): lqaf208

  Abstract

  The precise regulation of gene transcription relies on promoters, and the selection of specific promoters for a particular gene is a key determinant of transcript diversity. However, the regulatory mechanisms governing promoter selection are not fully understood. G-quadruplexes (G4s) are unique DNA noncanonical secondary structures that have emerged as important regulators of gene expression. In this study, we systematically analyzed the relationship between G4 structures and alternative promoters (APs) in two cancer cell lines, K562 and HepG2, by integrating native elongating transcript-cap analysis of gene expression and G4 ChIP-seq datasets. We identified 573 differentially utilized APs (|fold change| > 2, false discovery rate < 0.05), 26% of which being associated with G4 structures within 100 base pairs. Notably, G4-associated promoters predominantly exhibited increased activity, suggesting that G4s generally promote AP selection. Furthermore, treatment with G4 ligands induced the generation of APs, suggesting that the stabilization of G4 structures may modulate AP usage. Collectively, these findings provide new insights into the G4-based mechanisms that regulate transcript isoform diversity.

  View details for DOI 10.1093/nargab/lqaf208

  View details for Web of Science ID 001651772800001

  View details for PubMedID 41480590

  View details for PubMedCentralID PMC12754776

• Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma CANCERS Gu, Y., Huang, H., Tong, Q., Cao, M., Ming, W., Zhang, R., Zhu, W., Wang, Y., Sun, X. 2023; 15 (8)

  Abstract

  Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies worldwide, and the pronounced intra- and inter-tumor heterogeneity restricts clinical benefits. Dissecting molecular heterogeneity in HCC is commonly explored by endoscopic biopsy or surgical forceps, but invasive tissue sampling and possible complications limit the broadeer adoption. The radiomics framework is a promising non-invasive strategy for tumor heterogeneity decoding, and the linkage between radiomics and immuno-oncological characteristics is worth further in-depth study. In this study, we extracted multi-view imaging features from contrast-enhanced CT (CE-CT) scans of HCC patients, followed by developing a fused imaging feature subtyping (FIFS) model to identify two distinct radiomics subtypes. We observed two subtypes of patients with distinct texture-dominated radiomics profiles and prognostic outcomes, and the radiomics subtype identified by FIFS model was an independent prognostic factor. The heterogeneity was mainly attributed to inflammatory pathway activity and the tumor immune microenvironment. The predominant radiogenomics association was identified between texture-related features and immune-related pathways by integrating network analysis, and was validated in two independent cohorts. Collectively, this work described the close connections between multi-view radiomics features and immuno-oncological characteristics in HCC, and our integrative radiogenomics analysis strategy may provide clues to non-invasive inflammation-based risk stratification.

  View details for DOI 10.3390/cancers15082338

  View details for Web of Science ID 000977075700001

  View details for PubMedID 37190266

  View details for PubMedCentralID PMC10137067