Chaihuang Qingfu Pills Protect Against Acute Pancreatitis—Associated Acute Lung Injury Through MMP9-NLRP3-Pyroptosis Pathway

Journal of Inflammation Research, Год журнала: 2025, Номер Volume 18, С. 2317 - 2338

Опубликована: Фев. 1, 2025

Severe acute pancreatitis associated with lung injury (SAP-ALI) is a critical condition high mortality rate. Investigating the pathogenesis of SAP-ALI and developing effective treatments are urgently needed. Chaihuang Qingfu Pills (CHQF), traditional Chinese medicine modified from Qingyi Decoction, has been approved for treating (AP). However, its role in underlying mechanisms remain unclear. 92 AP patients were enrolled to observe protective effect CHQF on AP-ALI. L-arginine was used establish animal model. UHPLC-MS/MS identify components absorbed into serum. Transcriptomics analysis, network pharmacology, proteomics approaches explore molecular mechanism. In vivo vitro experiments conducted validate relevant findings. Clinical data indicated reduced incidence ALI 58.33% 36.36% patients. Animal demonstrated that decreased mortality, attenuated organ damage, inhibited systemic inflammation pathological SAP mice. Differential expression analysis weighted gene co-expression (WGCNA) identified 146 SAP-related differentially expressed genes (DEGs) GSE194331 dataset. acquired 26 blood 271 therapeutic targets. Integrated obtained 52 core targets SAP. Proteomic 216 proteins treatment SAP-ALI. Joint found MMP9 NLRP3 only common Both confirmed levels pyroptosis alveolar macrophages (AMs) under conditions. Moreover, inhibitor suppressed AMs pyroptosis. exerted by inhibiting macrophage through MMP9-NLRP3 pathway, providing novel strategy

Язык: Английский

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Identification of novel biomarkers based on lipid metabolism-related molecular subtypes for moderately severe and severe acute pancreatitis

Lipids in Health and Disease, Год журнала: 2024, Номер 23(1)

Опубликована: Янв. 2, 2024

Abstract Background Acute pancreatitis (AP) is an unpredictable and potentially fatal disorder. A derailed or unbalanced immune response may be the root of disease’s severe course. Disorders lipid metabolism are highly correlated with occurrence severity AP. We aimed to characterize contribution immunological characteristics metabolism-related genes (LMRGs) in non-mild acute (NMAP) identify a robust subtype biomarker for NMAP. Methods The expression mode LMRGs NMAP were examined. Then LMRG-derived subtypes identified using consensus clustering. weighted gene co-expression network analysis (WGCNA) was utilized determine hub perform functional enrichment analyses. Multiple machine learning methods used build diagnostic model patients. To validate predictive effectiveness, nomograms, receiver operating characteristic (ROC), calibration, decision curve (DCA) used. Using set variation (GSVA) single-cell study biological roles genes. Results Dysregulated responses between normal individuals. individuals divided into two LMRG-related significant differences function. cluster-specific primarily engaged regulation defense response, T cell activation, positive cytokine production. Moreover, we constructed two-gene prediction good performance. CARD16 MSGT1 significantly increased samples positively neutrophil mast infiltration. GSVA results showed that they mainly upregulated receptor complex, immunoglobulin complex circulating, some immune-related routes. Single-cell indicated distributed mixed cells macrophages, MGST1 exocrine glandular cells. Conclusions This presents novel approach categorizing different clusters based on developing reliable

Язык: Английский

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An integrated ensemble learning technique for gene expression classification and biomarker identification from RNA-seq data for pancreatic cancer prognosis

International Journal of Information Technology, Год журнала: 2024, Номер 16(3), С. 1505 - 1516

Опубликована: Янв. 16, 2024

Язык: Английский

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Genome Mining for Hub Genes Related to Endoplasmic Reticulum Stress in Pancreatitis: A Perspective from In Silico Characterization

Molecular Biotechnology, Год журнала: 2025, Номер unknown

Опубликована: Март 12, 2025

Язык: Английский

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Tumor treating fields enhance anti-PD therapy by improving CCL2/8 and CXCL9/CXCL10 expression through inducing immunogenic cell death in NSCLC models

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Март 17, 2025

Non-small cell lung cancer (NSCLC) is the most common type of cancer. Tumor treating fields (TTFields) combined with anti-PD immunotherapy offers a promising strategy to address this issue. Nevertheless, mechanism action (MOA) TTFields therapy in NSCLC has not been thoroughly investigated. This study aims elucidate MOA from aspect improving tumor immune microenvironment (TIME). Using mouse model NSCLC, we tested efficacy anti-PD-1 and anti-PD-L1 immunotherapy. By RNA-seq, differential genes signaling pathways between combination groups were studied. In-vitro experiments validated effects on cells for CD4+ T CD8+ infiltration, as well expression immunogenic death related chemokines. Combining reduced weight volume, respectively, compared controls (p < 0.05). RNA-seq analysis revealed 1,745 differentially expressed (DEGs) group versus controls, including upregulated (ICD) associated genes. Further showed that resulted increased infiltration alone, induced higher level ATP, HMGB1, CCL2, CCL8, CXCL9, CXCL10 inflammatory cytokines than control group. These collectively contributed altered TIME, finally potentiated therapy. enhance effectiveness by via inducing ICD increase CCL2/8 CXCL9/CXCL10 cells. provides theoretical basis new insights evaluating NSCLC.

Язык: Английский

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Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches

Human Vaccines & Immunotherapeutics, Год журнала: 2024, Номер 20(1)

Опубликована: Март 11, 2024

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation underlying mechanisms. Utilizing GEO, we analyzed GSE217948 with control samples. Differential expression analysis identified 16 differentially expressed genes, Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified cell infiltration. classification yielded two clusters, Weighted Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature validated by GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as top gene, corroborated Area Under Curve (AUC) analysis. Set Enrichment (GSEA) Variation (GSVA) revealed enriched pathways in T receptor, natural killer cytotoxicity, Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered infiltration differences, notably CD8 cells M0 macrophages. Clustering modules potential implications for COVID-19. effectively predicted COVID-19 risk, FDX1's pivotal role validated. high was associated pathways, suggesting its pathogenesis. comprehensive approach elucidated COVID-19-related context, risk prediction potential. connection offers insights into mechanisms therapy.

Язык: Английский

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Classification patterns identification of immunogenic cell death-related genes in heart failure based on deep learning

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 15, 2025

Heart failure (HF) is a complex and prevalent condition, particularly in the elderly, presenting symptoms like chest tightness, shortness of breath, dyspnea. The study aimed to improve classification HF subtypes identify potential drug targets by exploring role Immunogenic Cell Death (ICD), process known for its tumor immunity but underexplored research. Additionally, sought apply deep learning models enhance diagnosis-related genes. Various encoder were employed evaluate their effectiveness clustering based on ICD-related Identified further refined using differentially expressed genes, allowing assessment immune infiltration functional enrichment. Advanced machine techniques used these genes construct nomogram models. also explored gene interactions with miRNA transcription factors. Distinct identified through Differentially revealed significant variations enrichment across subtypes. diagnostic model showed excellent performance, an AUC exceeding 0.99 both internal external test sets. Diagnosis-related identified, serving as foundation exploration regulatory interactions. This provides novel insight into combining ICD, application models, identification These findings contribute deeper understanding highlight therapeutic improving treatment.

Язык: Английский

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Identification of subtypes and biomarkers associated with disulfidptosis-related ferroptosis in ulcerative colitis

Hereditas, Год журнала: 2025, Номер 162(1)

Опубликована: Фев. 22, 2025

Abstract Background Disulfidptosis and ferroptosis are different programmed cell death modes, which closely related to the development of a variety diseases, but relationship between them ulcerative colitis (UC) is still unclear. Therefore, our study aimed explore molecular subtypes biomarkers associated with disulfidptosis-related (DRF) in UC. Methods We used Pearson analysis identify DRF genes. Then, we classified 140 UC samples into based on genes explored biological clinical characteristics them. Next, hub were identified by differential WGCNA algorithms, three machine learning algorithms screen for from In addition, analyzed immune cells transcription factors predicted natural compounds that might be treat Finally, further verified reliability markers RT-qPCR experiments. Results 118 using analysis. Based expression level genes, patients C1 C2 subtypes, significant differences abundance infiltration disease activity two subtypes. The biomarkers, including XBP1, FH, MAP3K5. Further analyses revealed factors. six corresponding predicted, may contribute effective treatment trends MAP3K5 animal experiments consistent results bioinformatics Conclusion this study, systematically elucidated role UC, potential providing new idea diagnosis

Язык: Английский

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From Gene to Intervention: NLRC4 and WIPI1 Regulate Septic Acute Lung Injury Through Autophagy

Journal of Inflammation Research, Год журнала: 2025, Номер Volume 18, С. 3639 - 3656

Опубликована: Март 1, 2025

Background: Septic Acute Lung Injury (SALI)-induced severe respiratory dysfunction has been established to significantly increase patient mortality rates and socioeconomic costs. To mitigate cellular damage, autophagy —a conserved biological process in organisms —degrades damaged components, such as proteins organelles. Although is crucially involved the inflammatory response, its precise molecular mechanisms SALI remain unclear, forming basis of this study. Methods: Herein, two microarray datasets (GSE33118 GSE131761) three single-cell sequencing (SCP43, SCP548, SCP2156) derived from human samples were used ascertain interrelationship between Differentially Expressed Autophagy-Related Genes (DEARGs) SALI. The relationship key DEARGs was validated both vitro vivo using various techniques, including flow cytometry, Immunofluorescence (IF), Quantitative Polymerase Chain Reaction (qPCR), Western Blotting (WB), small interfering RNA (siRNA). Results: we found that activation attenuated SALI, with NLRC4 WIPI1 involved. Specifically, downregulation mitigated via activation. Compared NLRC4, more closely associated noncanonical autophagic flux Furthermore, immune infiltration analysis data showed a close WIPI1, cells. Conclusion: Our findings revealed correlated strongly autophagy, DEARGs, attenuating sepsis lung injury regulation, highlighting their therapeutic significance Keywords: septic acute injury, immunity, bioinformatics

Язык: Английский

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Identification and Validation of the Potential Key Biomarkers for Atopic Dermatitis Mitochondrion by Learning Algorithms

Journal of Inflammation Research, Год журнала: 2025, Номер Volume 18, С. 4291 - 4306

Опубликована: Март 1, 2025

Atopic dermatitis (AD) is a common inflammatory skin condition characterized by erythema and pruritus. Its precise pathogenesis remains unclear, though factors such as genetic predisposition, autoantigen response, allergen exposure, infections, barrier dysfunction are involved. Research suggests correlation between AD mitochondrial dysfunction, well oxidative stress in tissues. Skin sample datasets related to (GSE36842, GSE120721, GSE16161, GSE121212) were retrieved from the GEO database. Differential gene analysis identified differentially expressed genes (DEGs) AD. Three potential biomarkers-COX17, ACOX2, ADH1B-were using LASSO Support Vector Machine (SVM) algorithms. These biomarkers validated through ROC curve analysis, nomogram modeling, calibration curves, real-time PCR. Immune infiltration assessed correlations of biomarkers. Additionally, single-cell GSE153760 dataset nine cell clusters confirmed expression patterns three hub genes. 150 upregulated 367 downregulated Enrichment revealed significant pathways function, stress, energy metabolism samples patients. Area under (AUC) values for COX17, ADH1B 1.000, 0.928, 0.895, respectively, indicating strong predictive capacity. qPCR results showed COX17 was highly lesions, while ACOX2 higher normal skin, consistent with previous findings. Correlation indicated positively correlated resting mast cells but negatively activated T NK cells, positive negative cells. This study that may serve findings could provide insights treatment prognosis conditions.

Язык: Английский

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