Search PubMedSearch

Biomedical subjects

Shuqun Zhang

Publications and source records attributed to Shuqun Zhang.

2 recordsLinked to original sources

ZUP1 as a Novel Potential Oncogenic Driver and Prognostic Biomarker in Breast Cancer.

INTRODUCTION: Breast cancer is one of the main causes of cancer death in women globally. Identifying new predictive markers and therapeutic targets is important for improving patient outcomes. Zinc finger-containing U-rich RNA-binding protein 1 (ZUP1) is an RNA-binding protein containing a zinc finger structure that has not been systematically analyzed in breast cancer research. MATERIALS AND METHODS: The study used data from 1,231 samples from the Cancer Genome Atlas (TCGA) database. The ZUP1 expression in tumor tissues and normal tissues was compared. Its predictive value was assessed using survival analysis and regression models. Its biological role was explored through gene functional analysis. The immune cell analysis method was used to study the tumor immune environment, and the drug susceptibility database was used to predict drug responses. Predictive models were also built and validated. RESULTS: ZUP1 expression was significantly higher in breast cancer tissues than in normal tissues. High expression of ZUP1 is related to advanced tumor stage and is an independent indicator of poor survival prognosis in univariate and multivariate analyses. Functional enrichment revealed that ZUP1 is closely linked to cell cycle progression, DNA replication, and the Fanconi anemia (FA) pathway. Immune infiltration analysis demonstrated a significant negative link between ZUP1 levels and the abundance of resting mast cells and activated NK cells. Furthermore, high ZUP1 expression was associated with increased sensitivity to several targeted therapies, including Nutlin-3a and PD-0325901. A clinically applicable nomogram combining ZUP1 expression with key clinical factors (age, stage, T, N, M) was developed to predict 3- and 5-year OS with good calibration and discrimination. DISCUSSION: Our study identifies ZUP1 as a potential oncogenic factor and a robust independent prognostic biomarker in breast cancer. Its involvement in critical cellular processes and modulation of the tumor immune microenvironment highlights its potential as a novel therapeutic target. Functional experiments, including immunohistochemical staining and CCK8 proliferation assays, further supported the oncogenic role of ZUP1. The established nomogram provides a valuable tool for personalized risk assessment and clinical decision-making. CONCLUSION: Our findings suggest that ZUP1 is a novel multifaceted biomarker with significant implications for personalized treatment strategies in breast cancer.

ZUP1

PScnv: personalized self-normalizing CNV detection with a hierarchical multi-phase framework.

MOTIVATION: Accurate detection of copy number variations (CNVs) from targeted panel sequencing remains challenging due to limited genomic coverage and pronounced sample-specific biases. Existing normalization strategies, including baseline-cohort, matched-control, and single-sample approaches, often struggle to balance noise suppression with adaptability, leading to inconsistent performance across heterogeneous samples. RESULTS: We present PScnv, a personalized self-normalizing framework for robust CNV detection from panel sequencing data. PScnv integrates a pre-built panel-of-normals (PoN) with sample-intrinsic stable chromosomes through ridge-regression normalization to generate individualized log2 ratio profiles with reduced systematic variation. CNVs are then identified using a hierarchical multi-phase segmentation pipeline incorporating z-score pre-partitioning, kernel-based correction, and circular binary segmentation. In 139 clinical tumor samples with orthogonal FISH validation at MET, ERBB2, and MTAP, PScnv showed improved accuracy and robustness over existing methods that do not require patient-matched normal samples, provided that a pre-built PoN cohort is available. AVAILABILITY: Source code is available for academic use at https://github.com/lvws/PScnv.

DNA Copy Number Variations