Journal: Cancer Management and Research

Article Title: Phosphorylated MAPK14 Promotes the Proliferation and Migration of Bladder Cancer Cells by Maintaining RUNX2 Protein Abundance

doi: 10.2147/CMAR.S274058

Figure Lengend Snippet: P-MAPK14 regulates the RUNX2 protein expression in bladder cancer cells ( A ) After transfected with MAPK14 siRNAs in T24 and UMUC3, MAPK14 and RUNX2 mRNA levels were determined by RT-PCR; ( B ) RUNX2 protein levels in T24 and UMUC3 cells were measured by Western blot; ( C ) After T24 and UMUC3 cells were treated with VX-702, protein levels of MAPK14 , P-MAPK14 and RUNX2 were detected by Western blot; ( D ) MAPK14 and P-MAPK14 protein expression was detected by Western blot after overexpression of RUNX2 in T24 and UMUC3 cells; ( E ) The interaction between P-MAPK14 and RUNX2 was detected by Co-Immunoprecipitation assay in T24 and UMUC3 cells. (*P < 0.05, **P < 0.01, ***P < 0.001, ns P > 0.05).

Article Snippet: VX-702, cycloheximide (CHX) and MG-132 were purchased from MCE (MedChemExpress, USA).

Techniques: Expressing, Transfection, Reverse Transcription Polymerase Chain Reaction, Western Blot, Over Expression, Co-Immunoprecipitation Assay

Journal: The FASEB Journal

Article Title: Distinct cAMP Regulation in Scleroderma Lung and Skin Myofibroblasts Governs Their Dedifferentiation via p38α Inhibition

doi: 10.1096/fj.202500694RR

Figure Lengend Snippet: cAMP‐mediated p38 inhibition promotes dedifferentiation of lung and skin MFs. (A, B) Western blot and densitometric analysis of the phosphorylated proteins p‐p38, p‐ERK, and p‐JNK following SSc lung (A) and skin (B) MF treatment with forskolin (20 μM) for 6 h. Phosphorylated proteins were normalized to total levels of their respective proteins. (C) Western blot and densitometric analysis of the fibrosis‐associated genes Col1A1 and αSMA following SSc lung and skin MF treatment with SB203580 (20 μM) for 96 h. (D) αSMA stress fibers were identified by immunofluorescence microscopy using an anti‐αSMA‐FITC‐conjugated antibody (using the same protocol in C). Nuclei were stained with DAPI. Data points represent distinct patient‐derived cell lines. Significance for densitometric data ( n = 5–7) in (A and B) was determined by a 2‐tailed paired t‐test and by one‐way ANOVA in (C). * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.

Article Snippet: The pan‐p38 inhibitor SB203580 (20 μM) and p38α inhibitor VX‐702 (50 μM) were purchased from Cayman Chemicals (13067) and Selleckchem (HY‐10401), respectively.

Techniques: Inhibition, Western Blot, Immunofluorescence, Microscopy, Staining, Derivative Assay

Journal: The FASEB Journal

Article Title: Distinct cAMP Regulation in Scleroderma Lung and Skin Myofibroblasts Governs Their Dedifferentiation via p38α Inhibition

doi: 10.1096/fj.202500694RR

Figure Lengend Snippet: p38α inhibition promotes SSc lung and skin MF dedifferentiation. (A) qPCR data representing the relative expression of the genes encoding p38α, p38β, p38γ and p38δ in SSc lung and skin MFs. (B) Western blot and densitometric analysis of the fibrosis‐associated genes Col1A1 and αSMA following SSc lung and skin MF treatment with the isoform‐specific p38α inhibitor VX‐702 (50 μM) for 96 h. (C) αSMA stress fibers were identified by immunofluorescence microscopy using an anti‐αSMA‐FITC‐conjugated antibody (using the same protocol in B). Nuclei were stained with DAPI. Data points represent distinct patient‐derived cell lines. Significance for data in (A) ( n = 8) was determined by two‐tailed unpaired or paired t ‐test where appropriate and by one‐way ANOVA in (B) ( n = 6). * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.

Article Snippet: The pan‐p38 inhibitor SB203580 (20 μM) and p38α inhibitor VX‐702 (50 μM) were purchased from Cayman Chemicals (13067) and Selleckchem (HY‐10401), respectively.

Techniques: Inhibition, Expressing, Western Blot, Immunofluorescence, Microscopy, Staining, Derivative Assay, Two Tailed Test

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: Biomarker selection, correlation analysis, and functional similarity (Friend) analysis. A LASSO regression for prognostic gene selection. The optimal value of the lambda parameter is determined at the point with the minimum mean squared error on the curve. B Boxplots of candidate biomarker expression levels. The left panel shows expression in the training set, and the right panel shows expression in the validation set. C ROC curves of signature genes in the training set. The bottom-right legend lists each candidate biomarker along with its corresponding AUC value. D Correlation analysis of biomarkers in the IS training set, indicating that CXCL16 exhibits relatively strong functional similarity with other biomarkers. E Boxplot of functional similarity (Friend analysis) among biomarkers. Red represents ACTA2, green represents ST3GAL4, and blue represents CXCL16

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques: Biomarker Discovery, Selection, Functional Assay, Expressing

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: Construction and evaluation of the nomogram model. A Nomogram based on selected biomarkers. The “Total Points” represents the sum of the individual scores corresponding to each gene expression level, which can be mapped to the predicted probability of IS at the bottom of the figure. B Calibration curve of the nomogram. The Hosmer–Lemeshow test yielded a p-value > 0.05, indicating no significant difference between predicted and actual outcomes, and a good model fit. The mean absolute error (MAE) < 0.1 suggests minimal deviation between predicted and observed risk, supporting high predictive accuracy. C Decision curve analysis (DCA) for the biomarkers CXCL16, ST3GAL4, and ACTA2. All regression models showed positive net benefit, indicating that the nomogram provides good clinical utility. D Clinical impact curve (CIC). The CIC demonstrates that as the threshold probability increases, the number of predicted high-risk patients closely matches the actual cases, indicating improved clinical prediction efficiency. E ROC curve of the nomogram model, suggesting a certain diagnostic value for predicting IS

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques: Gene Expression, Diagnostic Assay

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: GSEA and GSVA enrichment analyses of prognostic genes. A GSEA results for CXCL16, showing enrichment of CXCL16-related genes in 67 pathways. B GSEA results for ST3GAL4, with 54 pathways enriched by ST3GAL4-associated genes. C GSEA results for ACTA2, identifying 55 enriched pathways related to ACTA2. D GSVA enrichment analysis of CXCL16, primarily enriched in 90 KEGG pathways including KEGG_MISMATCH_REPAIR. E GSVA enrichment analysis of ST3GAL4, primarily enriched in 90 KEGG pathways including KEGG_PRIMARY_IMMUNODEFICIENCY. F GSVA enrichment analysis of ACTA2, mainly enriched in 64 KEGG pathways including KEGG_BUTANOATE_METABOLISM

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques:

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: Drug prediction and molecular docking analysis. A Biomarker–drug interaction network. Red nodes represent biomarkers, and pink nodes represent predicted drug candidates. B–D Molecular docking models of key genes with their corresponding predicted drugs: B CXCL16 with benzene; C ST3GAL4 with Idose; D ACTA2 with probucol

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques: Biomarker Discovery

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: Molecular docking results between the proteins encoded by biomarkers and their respective inhibitors. A CXCL16 with Vx-702. B ST3GAL4 with Brigatinib. C ACTA2 with Y-27632

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques: