Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Transcriptomic profiling of tumor tissues introduces a large database, which has led to improvements in the ability of cancer diagnosis, treatment, and prevention. However, performing tumor transcriptomic profiling in the clinical setting is very challenging since the procurement of tumor tissues is inherently limited by invasive sampling procedures. Here, we demonstrated the feasibility of purifying hepatocellular carcinoma (HCC) circulating tumor cells (CTCs) from clinical patient samples with improved molecular integrity using Click Chips in conjunction with a multimarker antibody cocktail. The purified CTCs were then subjected to mRNA profiling by NanoString nCounter platform, targeting 64 HCC-specific genes, which were generated from an integrated data analysis framework with 8 tissue-based prognostic gene signatures from 7 publicly available HCC transcriptomic studies. After bioinformatics analysis and comparison, the HCC CTC-derived gene signatures showed high concordance with HCC tissue-derived gene signatures from TCGA database, suggesting that HCC CTCs purified by Click Chips could enable the translation of HCC tissue molecular profiling into a noninvasive setting.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240468 | PMC |
| http://dx.doi.org/10.1002/admt.202001056 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Distinct Evolutionary Signatures of Human Parainfluenza Viruses 2 and 4 Reveal Host Antagonism Divergence and Phylogenetic Discordance.
Mol Biol Evol
September 2025
Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, Washington, USA.
Human parainfluenza virus 2 (HPIV-2) and human parainfluenza virus 4 (HPIV-4) are significant but underappreciated respiratory pathogens, particularly among high-risk populations including children, the elderly, and immunocompromised individuals. In this study, we sequenced 101 HPIV-2 and HPIV-4 genomes from respiratory samples collected in western Washington State and performed comprehensive evolutionary analyses using both new and publicly available sequences. Phylogenetic and phylodynamic analyses revealed that both HPIV-2 and HPIV-4 evolve at significantly faster rates compared to mumps virus, a reference human orthorubulavirus.
View Article and Find Full Text PDFLoss of PTDSS1 in tumor cells improves immunogenicity and response to anti-PD-1 therapy.
Sci Adv
September 2025
Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
(phosphatidylserine synthase 1) encodes an enzyme that facilitates production of phosphatidylserine (PS), which mediates a global immunosuppressive signal. Here, based on in vivo CRISPR screen, we identified PTDSS1 as a target to improve anti-PD-1 therapy. Depletion of in tumor cells increased expression of interferon-γ (IFN-γ)-regulated genes, including , , , and , even in the absence of IFN-γ stimulation in vitro.
View Article and Find Full Text PDFPersonalizing chemotherapy drug selection using a novel transcriptomic chemogram.
PLoS Comput Biol
September 2025
Systems Biology and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America.
Gene signatures predictive of chemotherapeutic response have the potential to extend the reach of precision medicine by allowing oncologists to optimize treatment for individuals. Most published predictive signatures are only capable of predicting response for individual drugs, but most chemotherapy regimens utilize combinations of different agents. We propose a unified framework, called the chemogram, that uses predictive signatures to rank the relative predicted sensitivity of different drugs for individual tumors.
View Article and Find Full Text PDFIntegrating population-level and cell-based signatures for drug repositioning.
Bioinformatics
September 2025
The Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Motivation: Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Drugs with human genetic evidence are more likely to advance successfully through clinical trials towards FDA approval. Single gene-based drug repositioning methods have been implemented, but approaches leveraging a broad spectrum of molecular signatures remain underexplored.
View Article and Find Full Text PDFTransFactor-Prediction of pro-viral SARS-CoV-2 host factors using a protein language model.
Bioinformatics
September 2025
Computational Health Center, Helmholtz Center Munich, Neuherberg, 85764, Germany.
Motivation: Recent pandemics have revealed significant gaps in our understanding of viral pathogenesis, exposing an urgent need for methods to identify and prioritize key host proteins (host factors) as potential targets for antiviral treatments. De novo generation of experimental datasets is limited by their heterogeneity, and for looming future pandemics, may not be feasible due to limitations of experimental approaches.
Results: Here we present TransFactor, a computational framework for predicting and prioritizing candidate host factors using only protein sequence data.