Objectives: Pancreatic ductal adenocarcinoma (PDAC) has been subclassified into 3 molecular subtypes: classical, quasi-mesenchymal, and exocrine-like. These subtypes exhibit differences in patient survival and drug resistance to conventional therapies. The aim of the current study is to identify novel subtype-specific protein biomarkers facilitating subtype stratification of patients with PDAC and novel therapy development.
Methods: A set of 12 human patient-derived primary cell lines was used as a starting material for an advanced label-free proteomics approach leading to the identification of novel cell surface and secreted biomarkers. Cell surface protein identification was achieved by in vitro biotinylation, followed by mass spectrometric analysis of purified biotin-tagged proteins. Proteins secreted into a chemically defined serum-free cell culture medium were analyzed by shotgun proteomics.
Results: Of 3288 identified proteins, 2 pan-PDAC (protocadherin-1 and lipocalin-2) and 2 exocrine-like-specific (cadherin-17 and galectin-4) biomarker candidates have been validated. Proximity ligation assay analysis of the 2 exocrine-like biomarkers revealed their co-localization on the surface of exocrine-like cells.
Conclusions: The study reports the identification and validation of novel PDAC biomarkers relevant for the development of patient stratification tools. In addition, cadherin-17 and galectin-4 may serve as targets for bispecific antibodies as novel therapeutics in PDAC.
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http://dx.doi.org/10.1097/MPA.0000000000000743 | DOI Listing |
Mol Biotechnol
December 2024
Unit of Scientific Research, Applied College, Qassim University, Buraydah, 52571, Saudi Arabia.
The Zika virus (ZIKV), an arbovirus within the Flavivirus genus, is associated with severe neurological complications, including Guillain-Barré syndrome in affected individuals and microcephaly in infants born to infected mothers. With no approved vaccines or antiviral treatments available, there is an urgent need for effective therapeutic options. This study aimed to identify new natural compounds with inhibitory potential against the NS2B-NS3 protease (PDB ID: 5LC0), an essential enzyme in viral replication.
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December 2024
Division of Joint Surgery and Sports Medicine, Department of Orthopedic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Lines of evidence have indicated that type 2 diabetes mellitus (T2DM) is an independent risk factor for osteoarthritis (OA) progression. However, the study focused on the relationship between T2DM and OA at the transcriptional level remains empty. We downloaded OA- and T2DM-related bulk RNA-sequencing and single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) dataset.
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December 2024
Department of Ultrasound, The First Hospital of Hunan University of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410021, Hunan, People's Republic of China.
To develop and validate a nomogram for predicting the risk of adverse events (intraoperative massive haemorrhage or retained products of conception) associated with the termination of Caesarean scar pregnancy (CSP). Data were retrospectively collected from patients diagnosed with CSP who underwent Dilation and Curettage (D&C) at two hospitals. This data was divided into internal and external cohorts for analysis.
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December 2024
Department of Breast Surgery, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Breast cancer is a leading cause of cancer-related deaths among women globally. It is imperative to explore novel biomarkers to predict breast cancer treatment response as well as progression. Here, we collected six breast cancer samples and paired normal tissues for high-throughput sequencing.
View Article and Find Full Text PDFBehav Res Methods
December 2024
Department of Psychology, University of Milano-Bicocca, P.zza dell'Ateneo Nuovo, 1, 20126, Milano, Italy.
Despite being largely spoken and studied by language and cognitive scientists, Italian lacks large resources of language processing data. The Italian Crowdsourcing Project (ICP) is a dataset of word recognition times and accuracy including responses to 130,465 words, which makes it the largest dataset of its kind item-wise. The data were collected in an online word knowledge task in which over 156,000 native speakers of Italian took part.
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