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antibodies targeting p4hb  (Cell Signaling Technology Inc)


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    Structured Review

    Cell Signaling Technology Inc antibodies targeting p4hb
    Multiple machine learning algorithms were integrated to screen BLCA signature genes. (A) 10 different machine learning algorithms were incorporated to identify the genetic markers based on these 21 PCDGs. (B-D) 12 signature genes were identified by the CoxBoost algorithm and GBM algorithm. (E) The heat map revealed a significant upregulation of <t>P4HB,</t> ATP13A2, TFRC, TRAF7, and RRP12 in the high-risk group sample. (F) Patients with BLCA in the high-risk group had significantly lower OS compared to those in the low-risk group. (G) ROC curve showed excellent predictive accuracy for 1-, 3-, and 5-year interval risk scores in the TCGA-BLCA cohort
    Antibodies Targeting P4hb, supplied by Cell Signaling Technology Inc, used in various techniques. Bioz Stars score: 96/100, based on 366 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/antibodies targeting p4hb/product/Cell Signaling Technology Inc
    Average 96 stars, based on 366 article reviews
    antibodies targeting p4hb - by Bioz Stars, 2026-06
    96/100 stars

    Images

    1) Product Images from "Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation"

    Article Title: Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation

    Journal: Clinical and Experimental Medicine

    doi: 10.1007/s10238-026-02146-y

    Multiple machine learning algorithms were integrated to screen BLCA signature genes. (A) 10 different machine learning algorithms were incorporated to identify the genetic markers based on these 21 PCDGs. (B-D) 12 signature genes were identified by the CoxBoost algorithm and GBM algorithm. (E) The heat map revealed a significant upregulation of P4HB, ATP13A2, TFRC, TRAF7, and RRP12 in the high-risk group sample. (F) Patients with BLCA in the high-risk group had significantly lower OS compared to those in the low-risk group. (G) ROC curve showed excellent predictive accuracy for 1-, 3-, and 5-year interval risk scores in the TCGA-BLCA cohort
    Figure Legend Snippet: Multiple machine learning algorithms were integrated to screen BLCA signature genes. (A) 10 different machine learning algorithms were incorporated to identify the genetic markers based on these 21 PCDGs. (B-D) 12 signature genes were identified by the CoxBoost algorithm and GBM algorithm. (E) The heat map revealed a significant upregulation of P4HB, ATP13A2, TFRC, TRAF7, and RRP12 in the high-risk group sample. (F) Patients with BLCA in the high-risk group had significantly lower OS compared to those in the low-risk group. (G) ROC curve showed excellent predictive accuracy for 1-, 3-, and 5-year interval risk scores in the TCGA-BLCA cohort

    Techniques Used:

    Pseudotime trajectory analysis of P4HB in tumor cells using CytoTRACE2 and Monocle2. ( A ) Tumor cells were extracted and clustered into 14 distinct subgroups. (B) Expression of P4HB across the 14 tumor subgroups. (C) The differentiation status of tumor cells was visualized using CytoTRACE2, with colors representing the degree of differentiation. (D) The box plot shows the predicted ordering of tumor cell subpopulations by CytoTRACE2. (E-F) Pseudotime trajectory of tumor cells colored by pseudotime (E) and distribution of tumor cells (F). (G) Expression of P4HB across pseudotime. (H) Slingshot trajectory inference showing three distinct lineages originating from stem-like clusters. (I) Dynamic expression patterns of P4HB along each inferred lineage
    Figure Legend Snippet: Pseudotime trajectory analysis of P4HB in tumor cells using CytoTRACE2 and Monocle2. ( A ) Tumor cells were extracted and clustered into 14 distinct subgroups. (B) Expression of P4HB across the 14 tumor subgroups. (C) The differentiation status of tumor cells was visualized using CytoTRACE2, with colors representing the degree of differentiation. (D) The box plot shows the predicted ordering of tumor cell subpopulations by CytoTRACE2. (E-F) Pseudotime trajectory of tumor cells colored by pseudotime (E) and distribution of tumor cells (F). (G) Expression of P4HB across pseudotime. (H) Slingshot trajectory inference showing three distinct lineages originating from stem-like clusters. (I) Dynamic expression patterns of P4HB along each inferred lineage

    Techniques Used: Expressing

    Multiple analyses indicating a significant association between P4HB and the tumorigenesis as well as ROS pathway in BLCA. (A) Violin plot shows the differential expression of 12 hub genes between high- and low-risk score groups. (B) Boxplots show the overall expression levels of the 12 hub genes between cancer and normal tissues in the TCGA-BLCA cohort. (C) Paired difference analysis of 12 hub genes in the TCGA-BLCA cohort. (D) Radar map of P4HB expression levels in pan-cancer. (E) Immunohistochemical staining results from HPA database showed the expression of P4HB. (F) Risk regression analysis of P4HB in multiple bladder cancer cohorts (G) Differential expression analysis of P4HB in multiple progressive bladder cancer cohorts. (H) Differential expression analysis of P4HB in bladder cancer samples of different stages. (I) Expression density of P4HB at the single-cell level and cellular distribution of ROS pathway score
    Figure Legend Snippet: Multiple analyses indicating a significant association between P4HB and the tumorigenesis as well as ROS pathway in BLCA. (A) Violin plot shows the differential expression of 12 hub genes between high- and low-risk score groups. (B) Boxplots show the overall expression levels of the 12 hub genes between cancer and normal tissues in the TCGA-BLCA cohort. (C) Paired difference analysis of 12 hub genes in the TCGA-BLCA cohort. (D) Radar map of P4HB expression levels in pan-cancer. (E) Immunohistochemical staining results from HPA database showed the expression of P4HB. (F) Risk regression analysis of P4HB in multiple bladder cancer cohorts (G) Differential expression analysis of P4HB in multiple progressive bladder cancer cohorts. (H) Differential expression analysis of P4HB in bladder cancer samples of different stages. (I) Expression density of P4HB at the single-cell level and cellular distribution of ROS pathway score

    Techniques Used: Quantitative Proteomics, Expressing, Immunohistochemical staining, Staining, Single Cell

    P4HB expression and function in BLCA. (A) RT-qPCR results of P4HB mRNA expression in BLCA tissues from 25 paired adjacent normal and tumor samples. (B-C) P4HB mRNA (B) and protein (C) expression in BLCA cell lines (mean ± SD, n = 3, *** p < 0.001). (D-E) The interference efficiency of P4HB shRNA in BIU87 (D) and UMUC3 (E) was evaluated by RT-qPCR and western blot (mean ± SD, n = 3). (F-H) CCK-8 (F), colony assays (G), and flow cytometry (H) determined that blocking P4HB inhibited BLCA cells’ proliferation and growth, but promoted cells apoptosis (mean ± SD, n = 3, *** p < 0.001)
    Figure Legend Snippet: P4HB expression and function in BLCA. (A) RT-qPCR results of P4HB mRNA expression in BLCA tissues from 25 paired adjacent normal and tumor samples. (B-C) P4HB mRNA (B) and protein (C) expression in BLCA cell lines (mean ± SD, n = 3, *** p < 0.001). (D-E) The interference efficiency of P4HB shRNA in BIU87 (D) and UMUC3 (E) was evaluated by RT-qPCR and western blot (mean ± SD, n = 3). (F-H) CCK-8 (F), colony assays (G), and flow cytometry (H) determined that blocking P4HB inhibited BLCA cells’ proliferation and growth, but promoted cells apoptosis (mean ± SD, n = 3, *** p < 0.001)

    Techniques Used: Expressing, Quantitative RT-PCR, shRNA, Western Blot, CCK-8 Assay, Flow Cytometry, Blocking Assay



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    Image Search Results


    Multiple machine learning algorithms were integrated to screen BLCA signature genes. (A) 10 different machine learning algorithms were incorporated to identify the genetic markers based on these 21 PCDGs. (B-D) 12 signature genes were identified by the CoxBoost algorithm and GBM algorithm. (E) The heat map revealed a significant upregulation of P4HB, ATP13A2, TFRC, TRAF7, and RRP12 in the high-risk group sample. (F) Patients with BLCA in the high-risk group had significantly lower OS compared to those in the low-risk group. (G) ROC curve showed excellent predictive accuracy for 1-, 3-, and 5-year interval risk scores in the TCGA-BLCA cohort

    Journal: Clinical and Experimental Medicine

    Article Title: Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation

    doi: 10.1007/s10238-026-02146-y

    Figure Lengend Snippet: Multiple machine learning algorithms were integrated to screen BLCA signature genes. (A) 10 different machine learning algorithms were incorporated to identify the genetic markers based on these 21 PCDGs. (B-D) 12 signature genes were identified by the CoxBoost algorithm and GBM algorithm. (E) The heat map revealed a significant upregulation of P4HB, ATP13A2, TFRC, TRAF7, and RRP12 in the high-risk group sample. (F) Patients with BLCA in the high-risk group had significantly lower OS compared to those in the low-risk group. (G) ROC curve showed excellent predictive accuracy for 1-, 3-, and 5-year interval risk scores in the TCGA-BLCA cohort

    Article Snippet: After blocked, the membranes were then incubated overnight at 4 °C with primary antibodies targeting P4HB (dilution 1:1000, Cat. #3501, CST, USA).

    Techniques:

    Pseudotime trajectory analysis of P4HB in tumor cells using CytoTRACE2 and Monocle2. ( A ) Tumor cells were extracted and clustered into 14 distinct subgroups. (B) Expression of P4HB across the 14 tumor subgroups. (C) The differentiation status of tumor cells was visualized using CytoTRACE2, with colors representing the degree of differentiation. (D) The box plot shows the predicted ordering of tumor cell subpopulations by CytoTRACE2. (E-F) Pseudotime trajectory of tumor cells colored by pseudotime (E) and distribution of tumor cells (F). (G) Expression of P4HB across pseudotime. (H) Slingshot trajectory inference showing three distinct lineages originating from stem-like clusters. (I) Dynamic expression patterns of P4HB along each inferred lineage

    Journal: Clinical and Experimental Medicine

    Article Title: Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation

    doi: 10.1007/s10238-026-02146-y

    Figure Lengend Snippet: Pseudotime trajectory analysis of P4HB in tumor cells using CytoTRACE2 and Monocle2. ( A ) Tumor cells were extracted and clustered into 14 distinct subgroups. (B) Expression of P4HB across the 14 tumor subgroups. (C) The differentiation status of tumor cells was visualized using CytoTRACE2, with colors representing the degree of differentiation. (D) The box plot shows the predicted ordering of tumor cell subpopulations by CytoTRACE2. (E-F) Pseudotime trajectory of tumor cells colored by pseudotime (E) and distribution of tumor cells (F). (G) Expression of P4HB across pseudotime. (H) Slingshot trajectory inference showing three distinct lineages originating from stem-like clusters. (I) Dynamic expression patterns of P4HB along each inferred lineage

    Article Snippet: After blocked, the membranes were then incubated overnight at 4 °C with primary antibodies targeting P4HB (dilution 1:1000, Cat. #3501, CST, USA).

    Techniques: Expressing

    Multiple analyses indicating a significant association between P4HB and the tumorigenesis as well as ROS pathway in BLCA. (A) Violin plot shows the differential expression of 12 hub genes between high- and low-risk score groups. (B) Boxplots show the overall expression levels of the 12 hub genes between cancer and normal tissues in the TCGA-BLCA cohort. (C) Paired difference analysis of 12 hub genes in the TCGA-BLCA cohort. (D) Radar map of P4HB expression levels in pan-cancer. (E) Immunohistochemical staining results from HPA database showed the expression of P4HB. (F) Risk regression analysis of P4HB in multiple bladder cancer cohorts (G) Differential expression analysis of P4HB in multiple progressive bladder cancer cohorts. (H) Differential expression analysis of P4HB in bladder cancer samples of different stages. (I) Expression density of P4HB at the single-cell level and cellular distribution of ROS pathway score

    Journal: Clinical and Experimental Medicine

    Article Title: Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation

    doi: 10.1007/s10238-026-02146-y

    Figure Lengend Snippet: Multiple analyses indicating a significant association between P4HB and the tumorigenesis as well as ROS pathway in BLCA. (A) Violin plot shows the differential expression of 12 hub genes between high- and low-risk score groups. (B) Boxplots show the overall expression levels of the 12 hub genes between cancer and normal tissues in the TCGA-BLCA cohort. (C) Paired difference analysis of 12 hub genes in the TCGA-BLCA cohort. (D) Radar map of P4HB expression levels in pan-cancer. (E) Immunohistochemical staining results from HPA database showed the expression of P4HB. (F) Risk regression analysis of P4HB in multiple bladder cancer cohorts (G) Differential expression analysis of P4HB in multiple progressive bladder cancer cohorts. (H) Differential expression analysis of P4HB in bladder cancer samples of different stages. (I) Expression density of P4HB at the single-cell level and cellular distribution of ROS pathway score

    Article Snippet: After blocked, the membranes were then incubated overnight at 4 °C with primary antibodies targeting P4HB (dilution 1:1000, Cat. #3501, CST, USA).

    Techniques: Quantitative Proteomics, Expressing, Immunohistochemical staining, Staining, Single Cell

    P4HB expression and function in BLCA. (A) RT-qPCR results of P4HB mRNA expression in BLCA tissues from 25 paired adjacent normal and tumor samples. (B-C) P4HB mRNA (B) and protein (C) expression in BLCA cell lines (mean ± SD, n = 3, *** p < 0.001). (D-E) The interference efficiency of P4HB shRNA in BIU87 (D) and UMUC3 (E) was evaluated by RT-qPCR and western blot (mean ± SD, n = 3). (F-H) CCK-8 (F), colony assays (G), and flow cytometry (H) determined that blocking P4HB inhibited BLCA cells’ proliferation and growth, but promoted cells apoptosis (mean ± SD, n = 3, *** p < 0.001)

    Journal: Clinical and Experimental Medicine

    Article Title: Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation

    doi: 10.1007/s10238-026-02146-y

    Figure Lengend Snippet: P4HB expression and function in BLCA. (A) RT-qPCR results of P4HB mRNA expression in BLCA tissues from 25 paired adjacent normal and tumor samples. (B-C) P4HB mRNA (B) and protein (C) expression in BLCA cell lines (mean ± SD, n = 3, *** p < 0.001). (D-E) The interference efficiency of P4HB shRNA in BIU87 (D) and UMUC3 (E) was evaluated by RT-qPCR and western blot (mean ± SD, n = 3). (F-H) CCK-8 (F), colony assays (G), and flow cytometry (H) determined that blocking P4HB inhibited BLCA cells’ proliferation and growth, but promoted cells apoptosis (mean ± SD, n = 3, *** p < 0.001)

    Article Snippet: After blocked, the membranes were then incubated overnight at 4 °C with primary antibodies targeting P4HB (dilution 1:1000, Cat. #3501, CST, USA).

    Techniques: Expressing, Quantitative RT-PCR, shRNA, Western Blot, CCK-8 Assay, Flow Cytometry, Blocking Assay

    BAZ modulates PDI-dependent NOS dimerization in BRL-3A hepatocytes and primary mouse liver cells (A and B) iNOS protein levels and its dimer and monomer distribution in BRL-3A cells exposed for 8 h to 2 μM erastin ±1 μM BAZ (A) or for 4 h to 0.5 μM RSL3 ± 1 μM BAZ (B). Cellular proteins (25 μg) were resolved on 8% SDS-PAGE (reducing) or 6% SDS-PAGE (non-reducing) gels and immunoblotted with antibodies against iNOS and β-actin. (C and D) eNOS protein levels and its dimer and monomer distribution in BRL-3A cells after the same treatments with erastin (C) or RSL3 (D) as in (A) and (B). (E and F) iNOS protein levels and its dimer and monomer distribution in primary mouse liver cells treated for 2 h with 50 μM erastin ±10 μM BAZ (E) or 10 μM RSL3 ± 10 μM BAZ (F). (G and H) eNOS protein levels and its dimer and monomer distribution in primary mouse liver cells after the same treatments with erastin (G) or RSL3 (H) as in (E) and (F). (I and J) Cellular NO levels in BRL-3A cells after 8-h exposure to 2 μM erastin ±1 μM BAZ (I) or after 4 h exposure to 0.5 μM RSL3 ± 1 μM BAZ (J). Left panels are representative flow cytometry histograms; right panels are the respective quantitative intensity values ( n = 3). All quantitative data are presented as mean ± S.D. (∗∗ or ## p < 0.01).

    Journal: iScience

    Article Title: Bazedoxifene rescues hepatocytes from chemically induced oxidative ferroptotic injury in vivo and in vitro by inhibiting protein disulfide isomerase

    doi: 10.1016/j.isci.2026.114931

    Figure Lengend Snippet: BAZ modulates PDI-dependent NOS dimerization in BRL-3A hepatocytes and primary mouse liver cells (A and B) iNOS protein levels and its dimer and monomer distribution in BRL-3A cells exposed for 8 h to 2 μM erastin ±1 μM BAZ (A) or for 4 h to 0.5 μM RSL3 ± 1 μM BAZ (B). Cellular proteins (25 μg) were resolved on 8% SDS-PAGE (reducing) or 6% SDS-PAGE (non-reducing) gels and immunoblotted with antibodies against iNOS and β-actin. (C and D) eNOS protein levels and its dimer and monomer distribution in BRL-3A cells after the same treatments with erastin (C) or RSL3 (D) as in (A) and (B). (E and F) iNOS protein levels and its dimer and monomer distribution in primary mouse liver cells treated for 2 h with 50 μM erastin ±10 μM BAZ (E) or 10 μM RSL3 ± 10 μM BAZ (F). (G and H) eNOS protein levels and its dimer and monomer distribution in primary mouse liver cells after the same treatments with erastin (G) or RSL3 (H) as in (E) and (F). (I and J) Cellular NO levels in BRL-3A cells after 8-h exposure to 2 μM erastin ±1 μM BAZ (I) or after 4 h exposure to 0.5 μM RSL3 ± 1 μM BAZ (J). Left panels are representative flow cytometry histograms; right panels are the respective quantitative intensity values ( n = 3). All quantitative data are presented as mean ± S.D. (∗∗ or ## p < 0.01).

    Article Snippet: Samples were separated by SDS-PAGE, transferred to membranes, and probed with primary antibodies against PDI (#3501S, 1:3000; Cell Signaling Technology), iNOS (#ab178945, 1:3000; Abcam), eNOS (#ab76198, 1:3000; Abcam), and β-actin (#GB12001-100, 1:5000; ServiceBio, Wuhan, China).

    Techniques: SDS Page, Flow Cytometry

    CA inhibited inflammatory response via PDI mediated NLRP3 inflammasome pathway in vitro . (A) Expression levels and statistical analysis of NLRP3 inflammasome proteins and IL-1β in RAW 264.7 cells treated with CA (50 or 100 μM) for 3 hours, n=3, *** P <0.001, compared with Model group. (B) Expression levels and statistical analyses of PDI and NLRP3 inflammasome proteins in RAW 264.7 cells treated with CA (50 μM) and PDI-OE. (C-D) The levels of NO, TNF-α, and IL-1β released from RAW 264.7 cells treated with CA and/or PDI siRNA. (E) Expression levels of PDI and NLRP3 inflammasome proteins in the LPS-induced RAW 264.7 cells treated with CA (50 μM) for 3 hours and/or PDI siRNA for 48 hours, and Image J soft was used to quantify the intensity. n=3 in B-F, ns, not significant, * P<0.05, *** P<0.001.

    Journal: International Journal of Biological Sciences

    Article Title: Caffeic Acid Modulates Protein Disulfide Isomerase-NLRP3 Inflammasome Signaling to Mitigate Inflammation in Acute Pneumonia

    doi: 10.7150/ijbs.101061

    Figure Lengend Snippet: CA inhibited inflammatory response via PDI mediated NLRP3 inflammasome pathway in vitro . (A) Expression levels and statistical analysis of NLRP3 inflammasome proteins and IL-1β in RAW 264.7 cells treated with CA (50 or 100 μM) for 3 hours, n=3, *** P <0.001, compared with Model group. (B) Expression levels and statistical analyses of PDI and NLRP3 inflammasome proteins in RAW 264.7 cells treated with CA (50 μM) and PDI-OE. (C-D) The levels of NO, TNF-α, and IL-1β released from RAW 264.7 cells treated with CA and/or PDI siRNA. (E) Expression levels of PDI and NLRP3 inflammasome proteins in the LPS-induced RAW 264.7 cells treated with CA (50 μM) for 3 hours and/or PDI siRNA for 48 hours, and Image J soft was used to quantify the intensity. n=3 in B-F, ns, not significant, * P<0.05, *** P<0.001.

    Article Snippet: The following primary antibodies were used: rabbit-PDI (11245-1-AP), rabbit-NLRP3 (27458-1-AP), rabbit-ASC (10500-1-AP), rabbit-caspase-1 (22915-1-AP), and mouse anti-β-Actin (81115-1-RR) from Proteintech.

    Techniques: In Vitro, Expressing

    CA inhibited inflammatory response via PDI mediated NLRP3 inflammasome pathway in vivo . (A) Flow diagram of mice infected with PDI-KD AAV and treated with CA. (B) WB assay and densitometry analysis of proteins in lung tissues of mice with and without PDI-KD. (C) The expression levels of proteins in lung tissues of control or PDI-KD mice with CA treatment. (D-E) The levels of inflammatory cytokines in lung tissues (D) or serum (E) of PDI-KD mice after CA treatment. n=3, ns, not significant; *** P<0.001.

    Journal: International Journal of Biological Sciences

    Article Title: Caffeic Acid Modulates Protein Disulfide Isomerase-NLRP3 Inflammasome Signaling to Mitigate Inflammation in Acute Pneumonia

    doi: 10.7150/ijbs.101061

    Figure Lengend Snippet: CA inhibited inflammatory response via PDI mediated NLRP3 inflammasome pathway in vivo . (A) Flow diagram of mice infected with PDI-KD AAV and treated with CA. (B) WB assay and densitometry analysis of proteins in lung tissues of mice with and without PDI-KD. (C) The expression levels of proteins in lung tissues of control or PDI-KD mice with CA treatment. (D-E) The levels of inflammatory cytokines in lung tissues (D) or serum (E) of PDI-KD mice after CA treatment. n=3, ns, not significant; *** P<0.001.

    Article Snippet: The following primary antibodies were used: rabbit-PDI (11245-1-AP), rabbit-NLRP3 (27458-1-AP), rabbit-ASC (10500-1-AP), rabbit-caspase-1 (22915-1-AP), and mouse anti-β-Actin (81115-1-RR) from Proteintech.

    Techniques: In Vivo, Infection, Expressing, Control