Ruxolitinib enhances gastric cancer to chemotherapy by suppressing JAK/STAT3 and inducing mitochondrial dysfunction and oxidative stress

Immunopharmacology and Immunotoxicology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 9

Published: Feb. 26, 2025

Objective Chemoresistance in gastric cancer poses a major challenge treatment, necessitating the development of novel therapeutic strategies. This study evaluates efficacy ruxolitinib, JAK1/2 inhibitor, both sensitive and resistant cell lines.

Language: Английский

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Treatment of gouty lumbar spinal stenosis: a case report and bioinformatics analysis

BMC Musculoskeletal Disorders, Journal Year: 2025, Volume and Issue: 26(1)

Published: Jan. 17, 2025

The case of Lumbar spinal stenosis (LSS) combined with tophi due to gout is rarely reported. In the course our clinic work, we encountered a young male patient who was diagnosed history for 5 years and targeted as LSS gouty tophi, would like share this case. addition, in order further investigate deep mechanism associated gout, obtained intersecting genes two diseases based on machine learning approach by obtaining dataset GSE113212 related from Gene Expression Omnibus (GEO) database, human gene database. We found that TGFB1, PPARG, SAMRCC1 may be important biomarkers treating both diseases. From clinical perspective, clinicians should vigilant about possibility lumbar patients presenting back pain, hyperuricemia, elevated inflammatory markers. A surgical pharmacological treatment plan has favorable prognosis. Investigating mechanisms action core provide new insights treatment, ultimately leading development comprehensive personalized diagnostic therapeutic strategies.

Language: Английский

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Serum untargeted metabolomics combined with mouse models reveals potential mechanisms of ChengShu QingChu decoction for the treatment of vitiligo

Journal of Chromatography B, Journal Year: 2025, Volume and Issue: 1256, P. 124538 - 124538

Published: Feb. 23, 2025

Language: Английский

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Investigation of the Pathogenesis of Liver Fibrosis Associated with Type 2 Diabetes Mellitus via Bioinformatic Analysis

Biomedicines, Journal Year: 2025, Volume and Issue: 13(4), P. 840 - 840

Published: April 1, 2025

Background: The global prevalence of type 2 diabetes mellitus (T2DM) with liver fibrosis is rising, T2DM identified as an independent risk factor and key prognostic for fibrosis. However, the underlying mechanisms remain unclear. Methods: To explore shared pathogenesis T2DM, we analyzed gene expression profiles from GEO database. co-differentially expressed genes (co-DEGs) were subsequently through functional enrichment, protein–protein interaction (PPI) network construction, transcription prediction, drug prediction. Machine learning algorithms then applied to identify genes. Results: A total 175 co-DEGs identified. Functional enrichment analysis indicated their involvement in extracellular matrix (ECM) remodeling, inflammation, PI3K/Akt signaling pathway. Through PPI four algorithms, eight hub identified, including SPARC, COL4A2, THBS1, LUM, TIMP3, COL3A1, IGFBP7, FSTL1, THBS1 being recognized a by machine learning. upregulation was observed both diseases, it closely related progression T2DM. Transcription detected 29 regulators these Drug prediction suggested that retinoic acid may serve potential therapeutic agent. Conclusions: This study provides novel insights into offer targets clinical intervention.

Language: Английский

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Bioinformatics Combined With Biological Experiments to Identify the Pathogenetic Link of Type 2 Diabetes for Breast Cancer

Cancer Medicine, Journal Year: 2025, Volume and Issue: 14(7)

Published: April 1, 2025

ABSTRACT Background Type 2 diabetes mellitus (T2DM) constitutes a significant risk factor for breast cancer (BC), with affected women exhibiting two‐ to three‐fold increased likelihood of developing BC. Furthermore, diagnosed both BC and T2DM tend experience poorer prognoses exhibit greater resistance various treatments compared their non‐diabetic counterparts. Consequently, elucidating the comorbidities associated is instrumental in enhancing diagnostic therapeutic strategies Methods A series bioinformatics methods including weighted gene co‐expression network analysis (WGCNA), differentially expressed (DEG) analysis, machine learning, single‐cell sequencing were used identify pathogenetic molecules Biological experiments CCK‐8, colony formation, wound healing, transwell assay, immunohistochemistry, immunofluorescence performed determine molecule effect. Results By conducting WGCNA DEG on profiles (GSE25724 GSE20966) TCGA cohort BC, we identified total 27 common hub genes shared between These significantly enriched pathways related cell differentiation, cellular developmental processes, focal adhesion, MAPK signaling pathway. Notably, among these genes, CCNB2 , XRCC2 CENPI poor prognosis Moreover, RNA revealed that are cells within tissues. Additionally, observed CCNB2, XRCC2, elevated tissues provided by patients history KI67 expression. Hyperglycemia treatment expression levels cells, which correlated proliferation mobility. Conversely, knockdown partially mitigated pro‐proliferative pro‐migratory effects induced hyperglycemia cells. Conclusion Our findings suggested may serve as key pathogenic mediators linking Targeting could potentially attenuate adverse impacts progression.

Language: Английский

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In silico pan-cancer analysis of VRAC subunits and their prognostic roles in human cancers

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 11, 2025

Language: Английский

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Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer

Journal of Personalized Medicine, Journal Year: 2025, Volume and Issue: 15(5), P. 166 - 166

Published: April 24, 2025

Gastric cancer (GC) remains one of the leading causes cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC’s heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding GC by integrating molecular imaging data. In this review, we summarize current landscape ML-based integration for GC, highlighting its role precision diagnosis, prognosis prediction, biomarker discovery achieving personalized medicine.

Language: Английский

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Unveiling gastric precancerous stages: metabolomic insights for early detection and intervention

BMC Gastroenterology, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 29, 2025

Gastric precancerous lesions (GPL) represent a heterogeneous, multi-stage process that involves transition from benign to malignant state. To optimize prevention and intervention strategies, accurate methods must clearly distinguish between stages predict progression risks at early stages. The metabolomic profiles of 188 GPL tissues matched normal were characterized using ultra-high-performance liquid chromatography-tandem mass spectrometry. Both multivariate univariate statistical analyses used identify features differentiating normal, atrophic, intestinal metaplasia states in the stomach, followed by preliminary functional validation. From experiments conducted on two cohorts, we established reliable clinical gastric tissue map, which distinguished intestinalized tissues. We then identified metabolic biomarkers differentiated various Furthermore, key metabolites validated vitro studies. Relative acyl group glycerophospholipid abundance was higher when compared GPL, whereas organic acids more prevalent than A combination glycerophosphocholine, tiglylcarnitine, malate, sphingosine, γ-glutamylglutamic acid may serve as powerful GPL. chromatography with tandem spectrometry effectively characterize samples. Key targeted metabolomics. This study atrophy mucosa, uncovering preliminarily validating be assess high-risk populations diagnose potentially advancing cancer treatment efforts.

Language: Английский

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Value of Bioinformatics Models for Predicting Translational Control of Angiogenesis

Circulation Research, Journal Year: 2025, Volume and Issue: 136(10), P. 1147 - 1165

Published: May 8, 2025

Angiogenesis, the formation of new blood vessels, is a fundamental biological process with implications for both physiological functions and pathological conditions. While transcriptional regulation angiogenesis, mediated by factors such as HIF-1α (hypoxia-inducible factor 1-alpha) VEGF (vascular endothelial growth factor), well-characterized, translational this remains underexplored. Bioinformatics has emerged an indispensable tool advancing our understanding regulation, offering predictive models that leverage large data sets to guide research optimize experimental approaches. However, significant gap persists between bioinformatics experts other researchers, limiting accessibility utility these tools in broader scientific community. To address divide, user-friendly platforms are being developed democratize access analytics empower researchers across disciplines. Translational control, compared offers more energy-efficient mechanism facilitates rapid cellular responses environmental changes. Furthermore, regulators themselves often subject emphasizing interconnected nature regulatory layers. Investigating requires advanced, accessible analyze RNA structures, interacting micro-RNAs, long noncoding RNAs, RBPs (RNA-binding proteins). Predictive structure, human internal ribosome entry site Atlas, RBPSuite enable study motifs RNA-protein interactions, shedding light on critical mechanisms. This review highlights transformative role using widely Web-browser interface elucidate angiogenesis. The discussed extend beyond applications diverse fields, including clinical care. By integrating insights, can streamline hypothesis generation, reduce costs, find novel regulators. bridging knowledge gap, aims worldwide adopt their work, fostering innovation accelerating discovery.

Language: Английский

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Exploring the Spectrum of Long Non-Coding RNA CARMN in Physiological and Pathological Contexts

Biomolecules, Journal Year: 2024, Volume and Issue: 14(8), P. 954 - 954

Published: Aug. 6, 2024

Cardiac mesoderm enhancer-associated non-coding RNA (CARMN), an evolutionarily conserved long (lncRNA), serves as the host gene for miR143/145 cluster. It plays a crucial role in cardiovascular cell differentiation and maintenance of vascular smooth muscle (VSMC) homeostasis, which are vital normal physiological processes. Specifically, CARMN is associated with pathological progression diseases such atherosclerosis, abdominal aortic aneurysm, chronic heart failure. Moreover, it acts tumor suppressor various cancers, including hepatocellular carcinoma, bladder cancer, breast highlighting its potential beneficial biomarker therapeutic target. This review provides detailed examination roles CARMN, evolutionary conservation, expression patterns, regulatory mechanisms. also outlines significant implications diagnosis, prognosis, treatment these diseases, underscoring need further translational research to exploit clinical potential.

Language: Английский

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