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The stomach is an important digestive organ with various biological functions. However, because of the complexity of its cellular and glandular composition, its precise cellular biology has yet to be elucidated. In this study, we conducted single-cell RNA sequencing (scRNA-seq) and subcellular-level spatial transcriptomics analysis of the human stomach and constructed the largest dataset to date: a stomach encyclopedia. This dataset consists of approximately 380,000 cells from scRNA-seq and the spatial transcriptome, enabling integrated analyses of transcriptional and spatial information of gastric and metaplastic cells. This analysis identified LEFTY1 as an uncharacterized stem cell marker, which was confirmed through lineage tracing analysis. A wide variety of cell-cell interactions between epithelial and stromal cells, including PDGFRABMP4WNT5A fibroblasts, was highlighted in the developmental switch of intestinal metaplasia. Our extensive dataset will function as a fundamental resource in investigations of the stomach, including studies of development, aging, and carcinogenesis.
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http://dx.doi.org/10.1016/j.celrep.2023.113236 | DOI Listing |
Exp Ther Med
October 2025
Department of Surgery, Shanghai Jiading District Hospital of Traditional Chinese Medicine, Shanghai 201800, P.R. China.
The aim of the present study was to characterize the metabolomic signatures associated with colorectal polyps (CPs) in the gut. A metabolomics analysis was conducted on fecal samples collected from patients diagnosed with CPs as well as from healthy participants. A total of 60 participants were selected for analysis, including 30 patients diagnosed with CPs (CP group) and 30 healthy individuals serving as controls [healthy control (HC) group].
View Article and Find Full Text PDFHum Genomics
August 2025
Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Background: Gastric cancer (GC) ranks as the fifth most prevalent malignancy and the third leading cause of cancer-related mortality worldwide, with complex pathogenesis driven by genetic and epigenetic alterations. While genetic contributors to GC have been extensively studied, the functional role of N6-methyladenosine (m6A) RNA methylation-the most abundant eukaryotic RNA modification-in gastric carcinogenesis remains insufficiently characterized. This study aimed to investigate transcriptome-wide m6A methylation dysregulation and its mechanistic implications in GC progression.
View Article and Find Full Text PDFWorld J Gastrointest Oncol
August 2025
Chemoradiotherapy Center of Oncology, The Affiliated People's Hospital of Ningbo University, Ningbo 315000, Zhejiang Province, China.
Background: Gastric cancer (GC) has a high prevalence and mortality overall. is associated with abnormal centrosome amplification, DNA damage and increased apoptosis. To date, little is known about the function and mechanism of in GC.
View Article and Find Full Text PDFBiochem Biophys Res Commun
September 2025
Department of Biological Science and Technology, Jinzhong University, Jinzhong, 030619, China.
Gastric cancer (GC) is a common solid malignant tumor with a low 5-year survival rate. The underlying mechanism of cisplatin (DDP) resistance in GC cells remains obscure, and therapeutic targets have yet to be identified. Previously, we reported that saikosaponin D (SSD) could increase the DDP sensitivity of the GC cell line SGC-7901.
View Article and Find Full Text PDFDrug Des Devel Ther
July 2025
College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang, Henan, 471023, People's Republic of China.
Background: (SMW) can clear heat, expel dampness, tonify the kidney, strengthen the muscles, and treat damp heat in the spleen, stomach, and Lower Jiao. SMW is a formula commonly used for the clinical treatment of gout and hyperuricemia (HUA), especially asymptomatic HUA, and has shown remarkable therapeutic effects. This study aimed to investigate the therapeutic effect and mechanisms of SMW in a rat model of HUA using serum metabolomics.
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