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The tumor microenvironment and stemness score in high- and low-risk groups. A The correlation between the risk score and immune cell infiltration based on CIBERSORT analysis. B Comparisons of the ESTIMATE scores between the high- and low- risk groups. C Heatmap of immune checkpoints between high- and low-risk groups. D The association between risk score and angiogenesis-related genes. E The mRNAsi in different risk score groups. F-I The Spearman correlation analysis of mRNAsi with the expression of ALPK3( F ), SLC2A2( G ), CTSV( H ) and <t>PFKFB4(</t> I ). * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001
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The Role of <t>PFKFB4</t> in Small Cell Lung Cancer (SCLC). A) GEO database analysis of differentially expressed genes in lung cancer, with PFKFB4 highlighted in the red box. B) GEO lung cancer dataset analysis shows that the expression level of PFKFB4 in lung cancer is significantly higher than in normal tissue. C) GEO lung cancer dataset analysis reveals that higher expression of PFKFB4 correlates with poorer overall survival (OS) in lung cancer patients. D,E) Immunohistochemical analysis of PFKFB4 expression levels in adjacent non‐tumor and tumor tissues from small cell lung cancer (SCLC) patients, along with quantitative analysis. F,G) Single‐cell RNA sequencing analysis of the immune microenvironment in SCLC, identifying various cell types. H,I) Single‐cell analysis of PFKFB4 expression in CD8+ T cells and other cell types, with PFKFB4 predominantly expressed in macrophages. J) Schematic diagram of the 3D structure of paclitaxel. K,L) Docking of PFKFB4 with paclitaxel, shows that ASN64 of PFKFB4 interacts with paclitaxel via multiple forces, with a binding energy of −7.8 kcal mol −1 . M) Comparison of the IC50 values of paclitaxel before and after knockdown of PFKFB4 in NCI‐H446 and DMS114 SCLC cell lines. * p < 0.05, ** p < 0.01, *** p < 0.001.
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The Role of <t>PFKFB4</t> in Small Cell Lung Cancer (SCLC). A) GEO database analysis of differentially expressed genes in lung cancer, with PFKFB4 highlighted in the red box. B) GEO lung cancer dataset analysis shows that the expression level of PFKFB4 in lung cancer is significantly higher than in normal tissue. C) GEO lung cancer dataset analysis reveals that higher expression of PFKFB4 correlates with poorer overall survival (OS) in lung cancer patients. D,E) Immunohistochemical analysis of PFKFB4 expression levels in adjacent non‐tumor and tumor tissues from small cell lung cancer (SCLC) patients, along with quantitative analysis. F,G) Single‐cell RNA sequencing analysis of the immune microenvironment in SCLC, identifying various cell types. H,I) Single‐cell analysis of PFKFB4 expression in CD8+ T cells and other cell types, with PFKFB4 predominantly expressed in macrophages. J) Schematic diagram of the 3D structure of paclitaxel. K,L) Docking of PFKFB4 with paclitaxel, shows that ASN64 of PFKFB4 interacts with paclitaxel via multiple forces, with a binding energy of −7.8 kcal mol −1 . M) Comparison of the IC50 values of paclitaxel before and after knockdown of PFKFB4 in NCI‐H446 and DMS114 SCLC cell lines. * p < 0.05, ** p < 0.01, *** p < 0.001.
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The Role of <t>PFKFB4</t> in Small Cell Lung Cancer (SCLC). A) GEO database analysis of differentially expressed genes in lung cancer, with PFKFB4 highlighted in the red box. B) GEO lung cancer dataset analysis shows that the expression level of PFKFB4 in lung cancer is significantly higher than in normal tissue. C) GEO lung cancer dataset analysis reveals that higher expression of PFKFB4 correlates with poorer overall survival (OS) in lung cancer patients. D,E) Immunohistochemical analysis of PFKFB4 expression levels in adjacent non‐tumor and tumor tissues from small cell lung cancer (SCLC) patients, along with quantitative analysis. F,G) Single‐cell RNA sequencing analysis of the immune microenvironment in SCLC, identifying various cell types. H,I) Single‐cell analysis of PFKFB4 expression in CD8+ T cells and other cell types, with PFKFB4 predominantly expressed in macrophages. J) Schematic diagram of the 3D structure of paclitaxel. K,L) Docking of PFKFB4 with paclitaxel, shows that ASN64 of PFKFB4 interacts with paclitaxel via multiple forces, with a binding energy of −7.8 kcal mol −1 . M) Comparison of the IC50 values of paclitaxel before and after knockdown of PFKFB4 in NCI‐H446 and DMS114 SCLC cell lines. * p < 0.05, ** p < 0.01, *** p < 0.001.
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Image Search Results


The tumor microenvironment and stemness score in high- and low-risk groups. A The correlation between the risk score and immune cell infiltration based on CIBERSORT analysis. B Comparisons of the ESTIMATE scores between the high- and low- risk groups. C Heatmap of immune checkpoints between high- and low-risk groups. D The association between risk score and angiogenesis-related genes. E The mRNAsi in different risk score groups. F-I The Spearman correlation analysis of mRNAsi with the expression of ALPK3( F ), SLC2A2( G ), CTSV( H ) and PFKFB4( I ). * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001

Journal: BMC Gastroenterology

Article Title: Identification and validation of a lenvatinib resistance-related prognostic signature in HCC, in which PFKFB4 contributes to tumor progression and lenvatinib resistance

doi: 10.1186/s12876-025-03861-8

Figure Lengend Snippet: The tumor microenvironment and stemness score in high- and low-risk groups. A The correlation between the risk score and immune cell infiltration based on CIBERSORT analysis. B Comparisons of the ESTIMATE scores between the high- and low- risk groups. C Heatmap of immune checkpoints between high- and low-risk groups. D The association between risk score and angiogenesis-related genes. E The mRNAsi in different risk score groups. F-I The Spearman correlation analysis of mRNAsi with the expression of ALPK3( F ), SLC2A2( G ), CTSV( H ) and PFKFB4( I ). * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001

Article Snippet: A PFKFB4 siRNA (siPFKFB4) was designed by GenePharma (Shanghai GenePharma Co., Ltd. China).

Techniques: Expressing

Univariate and multivariate COX analyses of risk factors for OS in HCC patients

Journal: BMC Gastroenterology

Article Title: Identification and validation of a lenvatinib resistance-related prognostic signature in HCC, in which PFKFB4 contributes to tumor progression and lenvatinib resistance

doi: 10.1186/s12876-025-03861-8

Figure Lengend Snippet: Univariate and multivariate COX analyses of risk factors for OS in HCC patients

Article Snippet: A PFKFB4 siRNA (siPFKFB4) was designed by GenePharma (Shanghai GenePharma Co., Ltd. China).

Techniques: Expressing

PFKFB4 contributes to lenvatinib resistance in HCC. A , B qRT-PCR showed the up-regulation of PFKFB4 in LR cells. C , D Western blot showed the up-regulation of PFKFB4 protein in LR cells. Western blot was performed using full-length and unprocessed membranes. E , F Western blot analysis of PFKFB4 knockdown efficiency of three PFKFB4-targeting siRNA in LR cells. Western blot analyses were performed using intact membranes for Huh7-LR cells( E ), while stripped membranes was employed for experiments with HepG2-LR cells( F ). G , H Cell viability was evaluated by CCK8 in different groups. I , J Cell apoptosis was detected by flow cytometry analysis in different groups. * siNC vs. siPFKFB4, # siNC vs. siPFKFB4 + LVN, % siPFKFB4 vs. siPFKFB4 + LVN, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001; # P < 0.05, ## P < 0.01, ### P < 0.001; % P < 0.05, %% P < 0.01, %%% P < 0.001

Journal: BMC Gastroenterology

Article Title: Identification and validation of a lenvatinib resistance-related prognostic signature in HCC, in which PFKFB4 contributes to tumor progression and lenvatinib resistance

doi: 10.1186/s12876-025-03861-8

Figure Lengend Snippet: PFKFB4 contributes to lenvatinib resistance in HCC. A , B qRT-PCR showed the up-regulation of PFKFB4 in LR cells. C , D Western blot showed the up-regulation of PFKFB4 protein in LR cells. Western blot was performed using full-length and unprocessed membranes. E , F Western blot analysis of PFKFB4 knockdown efficiency of three PFKFB4-targeting siRNA in LR cells. Western blot analyses were performed using intact membranes for Huh7-LR cells( E ), while stripped membranes was employed for experiments with HepG2-LR cells( F ). G , H Cell viability was evaluated by CCK8 in different groups. I , J Cell apoptosis was detected by flow cytometry analysis in different groups. * siNC vs. siPFKFB4, # siNC vs. siPFKFB4 + LVN, % siPFKFB4 vs. siPFKFB4 + LVN, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001; # P < 0.05, ## P < 0.01, ### P < 0.001; % P < 0.05, %% P < 0.01, %%% P < 0.001

Article Snippet: A PFKFB4 siRNA (siPFKFB4) was designed by GenePharma (Shanghai GenePharma Co., Ltd. China).

Techniques: Quantitative RT-PCR, Western Blot, Knockdown, Flow Cytometry

The Role of PFKFB4 in Small Cell Lung Cancer (SCLC). A) GEO database analysis of differentially expressed genes in lung cancer, with PFKFB4 highlighted in the red box. B) GEO lung cancer dataset analysis shows that the expression level of PFKFB4 in lung cancer is significantly higher than in normal tissue. C) GEO lung cancer dataset analysis reveals that higher expression of PFKFB4 correlates with poorer overall survival (OS) in lung cancer patients. D,E) Immunohistochemical analysis of PFKFB4 expression levels in adjacent non‐tumor and tumor tissues from small cell lung cancer (SCLC) patients, along with quantitative analysis. F,G) Single‐cell RNA sequencing analysis of the immune microenvironment in SCLC, identifying various cell types. H,I) Single‐cell analysis of PFKFB4 expression in CD8+ T cells and other cell types, with PFKFB4 predominantly expressed in macrophages. J) Schematic diagram of the 3D structure of paclitaxel. K,L) Docking of PFKFB4 with paclitaxel, shows that ASN64 of PFKFB4 interacts with paclitaxel via multiple forces, with a binding energy of −7.8 kcal mol −1 . M) Comparison of the IC50 values of paclitaxel before and after knockdown of PFKFB4 in NCI‐H446 and DMS114 SCLC cell lines. * p < 0.05, ** p < 0.01, *** p < 0.001.

Journal: Advanced Science

Article Title: Targeting PFKFB4 Biomimetic Codelivery System Synergistically Enhances Ferroptosis to Suppress Small Cell Lung Cancer and Augments the Efficacy of Anti‐PD‐L1 Immunotherapy

doi: 10.1002/advs.202417374

Figure Lengend Snippet: The Role of PFKFB4 in Small Cell Lung Cancer (SCLC). A) GEO database analysis of differentially expressed genes in lung cancer, with PFKFB4 highlighted in the red box. B) GEO lung cancer dataset analysis shows that the expression level of PFKFB4 in lung cancer is significantly higher than in normal tissue. C) GEO lung cancer dataset analysis reveals that higher expression of PFKFB4 correlates with poorer overall survival (OS) in lung cancer patients. D,E) Immunohistochemical analysis of PFKFB4 expression levels in adjacent non‐tumor and tumor tissues from small cell lung cancer (SCLC) patients, along with quantitative analysis. F,G) Single‐cell RNA sequencing analysis of the immune microenvironment in SCLC, identifying various cell types. H,I) Single‐cell analysis of PFKFB4 expression in CD8+ T cells and other cell types, with PFKFB4 predominantly expressed in macrophages. J) Schematic diagram of the 3D structure of paclitaxel. K,L) Docking of PFKFB4 with paclitaxel, shows that ASN64 of PFKFB4 interacts with paclitaxel via multiple forces, with a binding energy of −7.8 kcal mol −1 . M) Comparison of the IC50 values of paclitaxel before and after knockdown of PFKFB4 in NCI‐H446 and DMS114 SCLC cell lines. * p < 0.05, ** p < 0.01, *** p < 0.001.

Article Snippet: In this study, a targeting PFKFB4 biomimetic codelivery system is developed to improve paclitaxel efficacy by inducing ferroptosis in SCLC.

Techniques: Expressing, Immunohistochemical staining, RNA Sequencing, Single-cell Analysis, Binding Assay, Comparison, Knockdown