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Gastric carcinoma (GC) is a severe tumor of the digestive tract with high morbidity and mortality and poor prognosis, for which novel treatment options are urgently needed. Compound Kushen injection (CKI), a classical injection of Chinese medicine, has been widely used to treat various tumors in clinical practice for decades. In recent years, a growing number of studies have confirmed that CKI has a beneficial therapeutic effect on GC, However, there are few reports on the potential molecular mechanism of action. Here, using systems pharmacology combined with proteomics analysis as a core concept, we identified the ceRNA network, key targets and signaling pathways regulated by CKI in the treatment of GC. To further explore the role of these key targets in the development of GC, we performed a meta-analysis to compare the expression differences between GC and normal gastric mucosa tissues. Functional enrichment analysis was further used to understand the biological pathways significantly regulated by the key genes. In addition, we determined the significance of the key genes in the prognosis of GC by survival analysis and immune infiltration analysis. Finally, molecular docking simulation was performed to verify the combination of CKI components and key targets. The anti-gastric cancer effect of CKI and its key targets was verified by and experiments. The analysis of ceRNA network of CKI on GC revealed that the potential molecular mechanism of CKI can regulate PI3K/AKT and Toll-like receptor signaling pathways by interfering with hub genes such as and . In conclusion, this study not only partially highlighted the molecular mechanism of CKI in GC therapy but also provided a novel and advanced systems pharmacology strategy to explore the mechanisms of traditional Chinese medicine formulations.
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http://dx.doi.org/10.3389/fcell.2021.742421 | DOI Listing |
J Med Internet Res
September 2025
School of Nursing, University of Minho, Braga, Portugal.
Background: The spread of misinformation on social media poses significant risks to public health and individual decision-making. Despite growing recognition of these threats, instruments that assess resilience to misinformation on social media, particularly among families who are central to making decisions on behalf of children, remain scarce.
Objective: This study aimed to develop and evaluate the psychometric properties of a novel instrument that measures resilience to misinformation in the context of social media among parents of school-age children.
Med Oncol
September 2025
Division of Hematology and Blood Bank, Department of Medical Laboratory Sciences, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Acute Myeloid Leukemia (AML) patient-derived Mesenchymal Stem Cells (MSCs) behave differently than normal ones, creating a more protective environment for leukemia cells, making relapse harder to prevent. This study aimed to identify prognostic biomarkers and elucidate relevant biological pathways in AML by leveraging microarray data and advanced bioinformatics techniques. We retrieved the GSE122917 dataset from the NCBI Gene Expression Omnibus and performed differential expression analysis (DEA) within R Studio to identify differentially expressed genes (DEGs) among healthy donors, newly diagnosed AML patients, and relapsed AML patients.
View Article and Find Full Text PDFCurr Rheumatol Rep
September 2025
Medical School of Hubei Enshi College, Enshi, 445000, China.
Cerebellum
September 2025
Neuropsychology and Applied Cognitive Neuroscience Laboratory, State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Reward processing involves several components, including reward anticipation, cost-effort computation, reward consumption, reward sensitivity, and reward learning. Recent research has highlighted the cerebellum's role in reward processing. This study aimed to investigate the effects of cerebellar stimulation on reward processing using high-definition transcranial direct current stimulation (HD-tDCS).
View Article and Find Full Text PDFMol Divers
September 2025
Department of Biotechnology, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492001, India.
Traditional drug discovery methods like high-throughput screening and molecular docking are slow and costly. This study introduces a machine learning framework to predict bioactivity (pIC₅₀) and identify key molecular properties and structural features for targeting Trypanothione reductase (TR), Protein kinase C theta (PKC-θ), and Cannabinoid receptor 1 (CB1) using data from the ChEMBL database. Molecular fingerprints, generated via PaDEL-Descriptor and RDKit, encoded structural features as binary vectors.
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