Biomedical subjects

Fan Zheng

Publications and source records attributed to Fan Zheng.

5 recordsLinked to original sources

PubMed2026

Non-invasive strategy for gastric cancer detection: Integration of cell-free DNA fragmentomics and protein biomarkers.

Gastric cancer (GC) ranks as the fifth most common cancer worldwide, however, accurate and non-invasive diagnostic modalities for GC remain limited. Cell-free DNA (cfDNA) fragmentomics has emerged as a promising tool for cancer cell detection. Here we develop a gastric cancer detection model, named GaFraD model. The GaFraD model uses four cfDNA fragmentomics features, including fragment size ratio (FSR), copy number variation (CNV), 9-bp end motif (Motif), and fragment size at transcription start sites (TF). This model achieves an area under the receiver-operating characteristic curve (AUC) of 0.970 (95% CI: 0.944 - 0.990), a sensitivity of 95.0% and a specificity of 80.9%. By combining the GaFraD model and conventional protein biomarkers CA19-9 and PG-I/PG-II, the CONFIRM model was generated. The CONFIRM model attained an AUC of 0.986 (95% CI: 0.966 - 1.000), a sensitivity of 95.0% and a specificity of 95.6% in detecting GC. Moreover, the CONFIRM model achieved remarkable performance (AUC = 0.983, sensitivity 95.6%, specificity 94.2%) in distinguishing patients with early-stage GC from controls. Our work showed the high discriminatory power in distinguishing GC patients from controls, indicating the clinical potential of using cfDNA fragmentomics combined with protein biomarkers for non-invasive GC detection. The results of the study provide a new avenue for early, accurate, and non-invasive clinical diagnosis of GC.

Cell-free DNA↗

PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗

PubMed2006

Experimental evidence for spin-split bands in a one-dimensional chain structure.

Gold atom chains on vicinal Si(111) surfaces exhibit an unusual doublet of half-filled bands, whose origin has remained uncertain. The splitting is identified by angle-resolved photoemission as a spin splitting induced by the spin-orbit interaction (Rashba effect), in agreement with a theoretical prediction by Sánchez-Portal, Riikonen, and Martin. This interaction leads to a characteristic pattern of avoided band crossings at a superlattice zone boundary. Two out of four crossings are avoided, with a minigap E_{G}=85 meV and a k offset of 0.05 A;{-1}.

Journal Article↗

PubMed2006

Characterization of protein immobilization at silver surfaces by near edge X-ray absorption fine structure spectroscopy.

Ribonuclease A (RNase A) is immobilized on silver surfaces in oriented and random form via self-assembled monolayers (SAMs) of alkanethiols. The immobilization process is characterized step-by-step using chemically selective near-edge X-ray absorption fine structure spectroscopy (NEXAFS) at the C, N, and S K-edges. Causes of imperfect immobilization are pinpointed, such as oxidation and partial desorption of the alkanethiol SAMs and incomplete coverage. The orientation of the protein layer manifests itself in an 18% polarization dependence of the NEXAFS signal from the N 1s to pi* transition of the peptide bond, which is not seen for a random orientation. The S 1s to C-S sigma* transition exhibits an even larger polarization dependence of 41%, which is reduced to 5% for a random orientation. A quantitative model is developed that explains the sign and magnitude of the polarization dependence at both edges. The results demonstrate that NEXAFS is able to characterize surface reactions during the immobilization of proteins and to provide insight into their orientations on surfaces.

Absorption↗

PubMed2006

Electron-phonon interaction at the Si(111)-7 x 7 surface.

It is shown that electron-phonon interaction provides a natural explanation for the unusual band dispersion of the metallic surface states at the Si(111)-(7 x 7) surface. Angle-resolved photoemission reveals a discontinuity of the adatom band at a binding energy close to the dominant surface phonon mode at h(omega0) = 70 meV. This mode has been assigned to adatom vibrations by molecular dynamics calculations. A calculation of the spectral function for electron-phonon interaction with this well-defined Einstein mode matches the data. Two independent determinations of the electron-phonon coupling parameter from the band dispersion and from the temperature-dependent phonon broadening yield similar values of lambda = 1.09 and lambda = 1.06.

Journal Article↗