Biomedical subjects

Jianqing Fan

Publications and source records attributed to Jianqing Fan.

5 recordsLinked to original sources

PubMed2026

A Rapid Poly(ethylene glycol)-Assisted Magnetic Isolation Approach for High-Throughput Extracellular Vesicle Isolation and Subsequent Biomarker Analysis.

Extracellular vesicles (EVs) are crucial mediators of intercellular communication and have the potential to serve as biomarkers for disease diagnosis and therapeutic monitoring. However, most EV isolation methods often require large sample volumes and specialized instruments or involve trade-offs between purity, yield, cost, and scalability. We developed MagPEG, a workflow that combines poly(ethylene glycol) (PEG)-mediated EV aggregation with magnetic beads to provide a simple, reproducible alternative to ultracentrifugation, size-exclusion chromatography, and commercial precipitation kits. Our optimization experiments clarified the PEG concentration, ionic strength, and bead surface chemistry that collectively influence EV aggregation, capture efficiency, and contaminant coprecipitation, allowing us to define conditions that improve purity while maintaining high recovery. Compared with commonly used methods, MagPEG produced EVs with comparable size distribution, EV markers, and proteomic profiles while relying only on standard laboratory supplies. A key feature of the platform is that EVs and EV-associated DNA, RNA, and proteins can be sequentially extracted from the same bead-bound material, reducing sample loss and hands-on time and enabling multiomic analysis for limited clinical or small animal samples. MagPEG is compatible with downstream applications including proteomics, bead-based assays, and miRNA quantification. When applied to human serum, the method supported high-throughput EV proteomic profiling and enabled the identification of Alzheimer's disease-associated protein signatures, illustrating its utility for biomarker discovery. Overall, our results establish MagPEG as a powerful, rapid, scalable, and high-throughput solution for translational applications in biomarker discovery.

Polyethylene Glycols↗

PubMed2006

Statistical analysis of DNA microarray data in cancer research.

Microarray techniques have been widely used to monitor gene expression in many areas of biomedical research. They have been widely used for tumor diagnosis and classification, prediction of prognoses and treatment, and understanding of molecular mechanisms, biochemical pathways, and gene networks. Statistical methods are vital for these scientific endeavors. This article reviews recent developments of statistical methods for analyzing data from microarray experiments. Emphasis has been given to normalization of expression from multiple arrays, selecting significantly differentially expressed genes, tumor classifications, and gene expression pathways and networks.

Cluster Analysis↗

PubMed2005

Removing intensity effects and identifying significant genes for Affymetrix arrays in macrophage migration inhibitory factor-suppressed neuroblastoma cells.

A semilinear in-slide model is introduced to remove the intensity effect in the scanning process. It is demonstrated that the intensity effect can be estimated accurately and removed effectively. This normalization step is vital for Affymetrix arrays to reveal relevant biological results when comparing gene expression in multiple arrays. The normalized expression ratios are analyzed further by a modified two-sample t test along with a sieved permutation scheme for computing P values. The improved specificity and sensitivity are demonstrated by using a study on the impact of macrophage migration inhibitory factor (MIF) reduction in neuroblastoma cells. With semilinear in-slide model analysis, expression of 166 genes was altered with a P value no greater than 0.001. Among those genes, 44 were altered >2-fold. MIF-regulated genes associated with tumor development including IL-8 and C-met, which are overexpressed in many tumors, were down-regulated in MIF-reduced cells. On the other hand, some tumor-suppressor genes such as EPHB6, visinin-like protein 1 (VSNL-1), and BLU were up-regulated in MIF-reduced cells. In addition, we demonstrated that down-regulation of MIF expression could result in a reduction in cell proliferation and tumor growth in vitro and in vivo. Our data not only demonstrate that targeting MIF expression is a promising therapeutic strategy in human neuroblastoma therapy but also indicate the MIF target genes for additional study.

Animals↗

PubMed2004

Normalization and analysis of cDNA microarrays using within-array replications applied to neuroblastoma cell response to a cytokine.

The quantitative comparison of two or more microarrays can reveal, for example, the distinct patterns of gene expression that define different cellular phenotypes or the genes that are induced in the cellular response to certain stimulations. Normalization of the measured intensities is a prerequisite of such comparisons. However, a fundamental problem in cDNA microarray analysis is the lack of a common standard to compare the expression levels of different samples. Several normalization protocols have been proposed to overcome the variabilities inherent in this technology. We have developed a normalization procedure based on within-array replications via a semilinear in-slide model, which adjusts objectively experimental variations without making critical biological assumptions. The significant analysis of gene expressions is based on a weighted t statistic, which accounts for the heteroscedasticity of the observed log ratios of expressions, and a balanced sign permutation test. We illustrated the use of the techniques in a comparison of the expression profiles of neuroblastoma cells that were stimulated with a growth factor, macrophage migration inhibitory factor (MIF). The analysis of expression changes at mRNA levels showed that approximately 99 genes were up-regulated and 24 were reduced significantly (P <0.001) in MIF-stimulated neuroblastoma cells. The regulated genes included several oncogenes, growth-related genes, tumor metastatic genes, and immuno-related genes. The findings provide clues as to the molecular mechanisms of MIF-mediated tumor progression and supply therapeutic targets for neuroblastoma treatment.

Gene Expression Profiling↗

PubMed2004

The use of proteomics in the discovery of serum biomarkers from patients with severe acute respiratory syndrome.

Severe acute respiratory syndrome (SARS) is a new infectious disease with a global impact. Understanding its pathogenesis and developing specific diagnostic methods for its early diagnosis are crucial for the effective management and control of this disease. By using proteomic technology, truncated forms of alpha(1)-antitrypsin (TF-alpha(1)-AT) were found to increase significantly and consistently in sera of SARS patients compared to control subjects. The result showed a sensitivity of 100% for SARS patients and a specificity of 92.8% for controls. Furthermore, the levels of these proteins significantly correlated with certain clinico-pathological parameters. The dramatic increase in TF-alpha(1)-AT may be the result of degradation of alpha(1)-AT. As alpha(1)-AT plays an important role in the protection of lung function, its degradation may be an important factor in the pathogenesis of SARS. These findings indicate that increased TF-alpha(1)-AT may be therapeutically relevant, and may also be a useful biological marker for the diagnosis of SARS.

Biomarkers↗