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tcga normalized rna-seq gene expression data from 33 cancer types  (Broad Institute Inc)

 
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    Broad Institute Inc tcga normalized rna-seq gene expression data from 33 cancer types
    Tcga Normalized Rna Seq Gene Expression Data From 33 Cancer Types, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/rna-seq+data/tcga+rna+seq+data/pmc06802116-254-8-14
    Average 90 stars, based on 1 article reviews
    tcga normalized rna-seq gene expression data from 33 cancer types - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Activation Assay:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    RNA Sequencing:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Activity Assay:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Microarray:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Expressing:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Immunofluorescence:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Generated:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Construct:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Immunopeptidomics:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Comparison:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA )

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.



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    Broad Clinical Labs colorectal tissue single cell rna sequencing scrna seq data
    A , scRNA-seq UMAP of <t>d35</t> CN colored by assigned cell type, excitatory (EXC) and inhibitory (INH) neuronal subtypes, glioblasts, and unassigned cells. The numbers of cells per cluster are shown in parentheses. B, Violin plots of Seurat-normalized expression levels for cell-identity marker genes. C, UMAPs of <t>d35</t> CNs exposed to different treatments (DMSO = blue; dTAG = red). D, DEGs per cell type after ES→CN depletion, as identified with Seurat FindMarkers (padj < 0.05, |log2FoldChange| > 0.5). E, Degree of similarity of combined DEGs identified in the indicated neuronal cell types, as indicated by log2FoldChange pairwise Pearson correlation. F, Functional enrichment (gProfiler; padj < 0.05) for ranked DEGs in the indicated neuronal subtypes. G, Violin plots showing subtype-specific (left) and shared (right) DEGs between neuronal subtypes. Upregulated DEGs highlighted in red and downregulated DEGs highlighted in blue. H, Impact of EHMT1 depletion on the neuronal maturation score in neuronal subtypes (see Methods). Mann-Whitney test with Bonferroni correction. I, As H for neuronal function score (see Methods). J, Transcription factors with regulons significantly affected by EHMT1 depletion (Mann-Whitney test with Bonferroni correction), as determined by SCENIC (see Methods). (+)(-) indicate positive and negative correlations between the expression of a transcription factor and its regulon, respectively. Arrows indicate significant (padj<0.05) log2FC (dTAG/DMSO) of the TF in the cell type.
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    A , scRNA-seq UMAP of <t>d35</t> CN colored by assigned cell type, excitatory (EXC) and inhibitory (INH) neuronal subtypes, glioblasts, and unassigned cells. The numbers of cells per cluster are shown in parentheses. B, Violin plots of Seurat-normalized expression levels for cell-identity marker genes. C, UMAPs of <t>d35</t> CNs exposed to different treatments (DMSO = blue; dTAG = red). D, DEGs per cell type after ES→CN depletion, as identified with Seurat FindMarkers (padj < 0.05, |log2FoldChange| > 0.5). E, Degree of similarity of combined DEGs identified in the indicated neuronal cell types, as indicated by log2FoldChange pairwise Pearson correlation. F, Functional enrichment (gProfiler; padj < 0.05) for ranked DEGs in the indicated neuronal subtypes. G, Violin plots showing subtype-specific (left) and shared (right) DEGs between neuronal subtypes. Upregulated DEGs highlighted in red and downregulated DEGs highlighted in blue. H, Impact of EHMT1 depletion on the neuronal maturation score in neuronal subtypes (see Methods). Mann-Whitney test with Bonferroni correction. I, As H for neuronal function score (see Methods). J, Transcription factors with regulons significantly affected by EHMT1 depletion (Mann-Whitney test with Bonferroni correction), as determined by SCENIC (see Methods). (+)(-) indicate positive and negative correlations between the expression of a transcription factor and its regulon, respectively. Arrows indicate significant (padj<0.05) log2FC (dTAG/DMSO) of the TF in the cell type.
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    Increased IFITM and decreased DHODH expression characterize the placenta in the context of HDPs (A) Volcano plot of transcriptomic changes identified by <t>RNA-seq.</t> Transcripts highlighted in red or blue were significantly altered ( q value < 0.05). (B) Differentially expressed genes were classified by functional enrichment analysis using the Reactome pathway database and Gene Ontology (GO) biological processes or cellular components. (C) Heatmap of mitochondria-related genes downregulated in the placenta in the context of HDPs. Genes with higher expression are shown in green, and those with lower expression are shown in red. Ctrl: premature delivery, n = 5; HDP, n = 5. (D) Expression of DHODH, OPA1, DNM1L, MFN1, TFAM, and IFITM1-3 in trophoblast BeWo cells treated with forskolin (FSK, 2.5 μM) and rotenone (Rote, 50 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗ p < 0.05, ∗∗ p < 0.01 vs. FSK alone (Tukey’s test). (E) Expression of IFITMs in BeWo cells treated with FSK (2.5 μM), orludodstat (Orlu, 1 nM), or brequinar (Bre, 25 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗∗ p < 0.01 vs. Ctrl; † p < 0.05, †† p < 0.01, ††† p < 0.001 vs. FSK alone (Tukey’s test).
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    Increased IFITM and decreased DHODH expression characterize the placenta in the context of HDPs (A) Volcano plot of transcriptomic changes identified by <t>RNA-seq.</t> Transcripts highlighted in red or blue were significantly altered ( q value < 0.05). (B) Differentially expressed genes were classified by functional enrichment analysis using the Reactome pathway database and Gene Ontology (GO) biological processes or cellular components. (C) Heatmap of mitochondria-related genes downregulated in the placenta in the context of HDPs. Genes with higher expression are shown in green, and those with lower expression are shown in red. Ctrl: premature delivery, n = 5; HDP, n = 5. (D) Expression of DHODH, OPA1, DNM1L, MFN1, TFAM, and IFITM1-3 in trophoblast BeWo cells treated with forskolin (FSK, 2.5 μM) and rotenone (Rote, 50 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗ p < 0.05, ∗∗ p < 0.01 vs. FSK alone (Tukey’s test). (E) Expression of IFITMs in BeWo cells treated with FSK (2.5 μM), orludodstat (Orlu, 1 nM), or brequinar (Bre, 25 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗∗ p < 0.01 vs. Ctrl; † p < 0.05, †† p < 0.01, ††† p < 0.001 vs. FSK alone (Tukey’s test).
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    Increased IFITM and decreased DHODH expression characterize the placenta in the context of HDPs (A) Volcano plot of transcriptomic changes identified by <t>RNA-seq.</t> Transcripts highlighted in red or blue were significantly altered ( q value < 0.05). (B) Differentially expressed genes were classified by functional enrichment analysis using the Reactome pathway database and Gene Ontology (GO) biological processes or cellular components. (C) Heatmap of mitochondria-related genes downregulated in the placenta in the context of HDPs. Genes with higher expression are shown in green, and those with lower expression are shown in red. Ctrl: premature delivery, n = 5; HDP, n = 5. (D) Expression of DHODH, OPA1, DNM1L, MFN1, TFAM, and IFITM1-3 in trophoblast BeWo cells treated with forskolin (FSK, 2.5 μM) and rotenone (Rote, 50 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗ p < 0.05, ∗∗ p < 0.01 vs. FSK alone (Tukey’s test). (E) Expression of IFITMs in BeWo cells treated with FSK (2.5 μM), orludodstat (Orlu, 1 nM), or brequinar (Bre, 25 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗∗ p < 0.01 vs. Ctrl; † p < 0.05, †† p < 0.01, ††† p < 0.001 vs. FSK alone (Tukey’s test).
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    Increased IFITM and decreased DHODH expression characterize the placenta in the context of HDPs (A) Volcano plot of transcriptomic changes identified by <t>RNA-seq.</t> Transcripts highlighted in red or blue were significantly altered ( q value < 0.05). (B) Differentially expressed genes were classified by functional enrichment analysis using the Reactome pathway database and Gene Ontology (GO) biological processes or cellular components. (C) Heatmap of mitochondria-related genes downregulated in the placenta in the context of HDPs. Genes with higher expression are shown in green, and those with lower expression are shown in red. Ctrl: premature delivery, n = 5; HDP, n = 5. (D) Expression of DHODH, OPA1, DNM1L, MFN1, TFAM, and IFITM1-3 in trophoblast BeWo cells treated with forskolin (FSK, 2.5 μM) and rotenone (Rote, 50 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗ p < 0.05, ∗∗ p < 0.01 vs. FSK alone (Tukey’s test). (E) Expression of IFITMs in BeWo cells treated with FSK (2.5 μM), orludodstat (Orlu, 1 nM), or brequinar (Bre, 25 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗∗ p < 0.01 vs. Ctrl; † p < 0.05, †† p < 0.01, ††† p < 0.001 vs. FSK alone (Tukey’s test).
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    Image Search Results


    A , scRNA-seq UMAP of d35 CN colored by assigned cell type, excitatory (EXC) and inhibitory (INH) neuronal subtypes, glioblasts, and unassigned cells. The numbers of cells per cluster are shown in parentheses. B, Violin plots of Seurat-normalized expression levels for cell-identity marker genes. C, UMAPs of d35 CNs exposed to different treatments (DMSO = blue; dTAG = red). D, DEGs per cell type after ES→CN depletion, as identified with Seurat FindMarkers (padj < 0.05, |log2FoldChange| > 0.5). E, Degree of similarity of combined DEGs identified in the indicated neuronal cell types, as indicated by log2FoldChange pairwise Pearson correlation. F, Functional enrichment (gProfiler; padj < 0.05) for ranked DEGs in the indicated neuronal subtypes. G, Violin plots showing subtype-specific (left) and shared (right) DEGs between neuronal subtypes. Upregulated DEGs highlighted in red and downregulated DEGs highlighted in blue. H, Impact of EHMT1 depletion on the neuronal maturation score in neuronal subtypes (see Methods). Mann-Whitney test with Bonferroni correction. I, As H for neuronal function score (see Methods). J, Transcription factors with regulons significantly affected by EHMT1 depletion (Mann-Whitney test with Bonferroni correction), as determined by SCENIC (see Methods). (+)(-) indicate positive and negative correlations between the expression of a transcription factor and its regulon, respectively. Arrows indicate significant (padj<0.05) log2FC (dTAG/DMSO) of the TF in the cell type.

    Journal: bioRxiv

    Article Title: Developmental rewiring of EHMT1 function during human cortical neurogenesis

    doi: 10.64898/2026.10.09.757098

    Figure Lengend Snippet: A , scRNA-seq UMAP of d35 CN colored by assigned cell type, excitatory (EXC) and inhibitory (INH) neuronal subtypes, glioblasts, and unassigned cells. The numbers of cells per cluster are shown in parentheses. B, Violin plots of Seurat-normalized expression levels for cell-identity marker genes. C, UMAPs of d35 CNs exposed to different treatments (DMSO = blue; dTAG = red). D, DEGs per cell type after ES→CN depletion, as identified with Seurat FindMarkers (padj < 0.05, |log2FoldChange| > 0.5). E, Degree of similarity of combined DEGs identified in the indicated neuronal cell types, as indicated by log2FoldChange pairwise Pearson correlation. F, Functional enrichment (gProfiler; padj < 0.05) for ranked DEGs in the indicated neuronal subtypes. G, Violin plots showing subtype-specific (left) and shared (right) DEGs between neuronal subtypes. Upregulated DEGs highlighted in red and downregulated DEGs highlighted in blue. H, Impact of EHMT1 depletion on the neuronal maturation score in neuronal subtypes (see Methods). Mann-Whitney test with Bonferroni correction. I, As H for neuronal function score (see Methods). J, Transcription factors with regulons significantly affected by EHMT1 depletion (Mann-Whitney test with Bonferroni correction), as determined by SCENIC (see Methods). (+)(-) indicate positive and negative correlations between the expression of a transcription factor and its regulon, respectively. Arrows indicate significant (padj<0.05) log2FC (dTAG/DMSO) of the TF in the cell type.

    Article Snippet: We conducted bioinformatic analysis of bulk RNA-seq data for d35 CN single-cell RNA-seq validation using Plasmidsaurus’s workflow.

    Techniques: Expressing, Marker, Functional Assay, MANN-WHITNEY

    Increased IFITM and decreased DHODH expression characterize the placenta in the context of HDPs (A) Volcano plot of transcriptomic changes identified by RNA-seq. Transcripts highlighted in red or blue were significantly altered ( q value < 0.05). (B) Differentially expressed genes were classified by functional enrichment analysis using the Reactome pathway database and Gene Ontology (GO) biological processes or cellular components. (C) Heatmap of mitochondria-related genes downregulated in the placenta in the context of HDPs. Genes with higher expression are shown in green, and those with lower expression are shown in red. Ctrl: premature delivery, n = 5; HDP, n = 5. (D) Expression of DHODH, OPA1, DNM1L, MFN1, TFAM, and IFITM1-3 in trophoblast BeWo cells treated with forskolin (FSK, 2.5 μM) and rotenone (Rote, 50 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗ p < 0.05, ∗∗ p < 0.01 vs. FSK alone (Tukey’s test). (E) Expression of IFITMs in BeWo cells treated with FSK (2.5 μM), orludodstat (Orlu, 1 nM), or brequinar (Bre, 25 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗∗ p < 0.01 vs. Ctrl; † p < 0.05, †† p < 0.01, ††† p < 0.001 vs. FSK alone (Tukey’s test).

    Journal: iScience

    Article Title: DHODH regulates trophoblast fusion via IFITM-reduced plasma membrane fluidity: Implications for hypertensive disorders of pregnancy

    doi: 10.1016/j.isci.2026.116163

    Figure Lengend Snippet: Increased IFITM and decreased DHODH expression characterize the placenta in the context of HDPs (A) Volcano plot of transcriptomic changes identified by RNA-seq. Transcripts highlighted in red or blue were significantly altered ( q value < 0.05). (B) Differentially expressed genes were classified by functional enrichment analysis using the Reactome pathway database and Gene Ontology (GO) biological processes or cellular components. (C) Heatmap of mitochondria-related genes downregulated in the placenta in the context of HDPs. Genes with higher expression are shown in green, and those with lower expression are shown in red. Ctrl: premature delivery, n = 5; HDP, n = 5. (D) Expression of DHODH, OPA1, DNM1L, MFN1, TFAM, and IFITM1-3 in trophoblast BeWo cells treated with forskolin (FSK, 2.5 μM) and rotenone (Rote, 50 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗ p < 0.05, ∗∗ p < 0.01 vs. FSK alone (Tukey’s test). (E) Expression of IFITMs in BeWo cells treated with FSK (2.5 μM), orludodstat (Orlu, 1 nM), or brequinar (Bre, 25 nM) for 48 h. GAPDH was used as a reference gene. The data are presented as the mean ± SEM from three independent experiments. ∗∗ p < 0.01 vs. Ctrl; † p < 0.05, †† p < 0.01, ††† p < 0.001 vs. FSK alone (Tukey’s test).

    Article Snippet: • Raw RNA-seq data derived from human placental samples and trophoblast cell lines have been deposited at the DNA DataBank of Japan (DDBJ) Sequence Read Archive as DDBJ: DRA021720 and DRA021721 and are publicly available as of the date of publication.

    Techniques: Expressing, RNA Sequencing, Functional Assay

    DHODH regulates IFITM expression via IRF1 (A–H) BeWo cells or DHODH-KD BeWo cells were treated with FSK (2.5 μM), Orlu (1 nM), or Bre (25 nM) for 48 h. (A) Volcano plot showing transcriptomic changes identified by RNA-seq. Transcripts highlighted in red or blue were considered differentially expressed, as indicated by an expression change ≥2-fold ( p < 0.05). (B) Correlation analysis of RNA-seq data from Orlu-, Bre-treated, and DHODH-KD cells. (C) Differentially expressed genes were classified by functional enrichment analysis using the Wiki pathway database and GO biological processes or cellular components. (D) RNA-seq was used to evaluate the expression levels of genes associated with syncytialization. (E) RNA-seq was used to evaluate the expression levels of IRF family genes. (F) Immunoblotting for IRF1, total IRF3, and p -IRF3. GAPDH was used as a loading control. Representative data from three independent experiments are shown. The graph shows the total IRF3 and p -IRF3 levels normalized to GAPDH levels from three independent experiments. ∗∗ p < 0.01, ∗∗∗ p < 0.001 vs. Ctrl (Tukey’s test). Values represent the mean ± SEM. (G) ChIP assay showing IRF1 binding to upstream regulatory regions (up to 3 kbp) of the IFITM1, IFITM2, and IFITM3 loci in BeWo cells treated with FSK alone (2.5 μM) for 48 h. ∗ p < 0.05 vs. Ctrl; † p < 0.05, †† p < 0.01, ††† p < 0.001 vs. FSK alone (Tukey’s test). (H) Immunofluorescence staining of IRF1 (red). Nuclei were counterstained with DAPI (blue). Scale bars, 5 μm. The graph shows the number of staining cells from three independent experiments. Values represent the mean ± SEM. ∗∗∗ p < 0.001 vs. FSK.

    Journal: iScience

    Article Title: DHODH regulates trophoblast fusion via IFITM-reduced plasma membrane fluidity: Implications for hypertensive disorders of pregnancy

    doi: 10.1016/j.isci.2026.116163

    Figure Lengend Snippet: DHODH regulates IFITM expression via IRF1 (A–H) BeWo cells or DHODH-KD BeWo cells were treated with FSK (2.5 μM), Orlu (1 nM), or Bre (25 nM) for 48 h. (A) Volcano plot showing transcriptomic changes identified by RNA-seq. Transcripts highlighted in red or blue were considered differentially expressed, as indicated by an expression change ≥2-fold ( p < 0.05). (B) Correlation analysis of RNA-seq data from Orlu-, Bre-treated, and DHODH-KD cells. (C) Differentially expressed genes were classified by functional enrichment analysis using the Wiki pathway database and GO biological processes or cellular components. (D) RNA-seq was used to evaluate the expression levels of genes associated with syncytialization. (E) RNA-seq was used to evaluate the expression levels of IRF family genes. (F) Immunoblotting for IRF1, total IRF3, and p -IRF3. GAPDH was used as a loading control. Representative data from three independent experiments are shown. The graph shows the total IRF3 and p -IRF3 levels normalized to GAPDH levels from three independent experiments. ∗∗ p < 0.01, ∗∗∗ p < 0.001 vs. Ctrl (Tukey’s test). Values represent the mean ± SEM. (G) ChIP assay showing IRF1 binding to upstream regulatory regions (up to 3 kbp) of the IFITM1, IFITM2, and IFITM3 loci in BeWo cells treated with FSK alone (2.5 μM) for 48 h. ∗ p < 0.05 vs. Ctrl; † p < 0.05, †† p < 0.01, ††† p < 0.001 vs. FSK alone (Tukey’s test). (H) Immunofluorescence staining of IRF1 (red). Nuclei were counterstained with DAPI (blue). Scale bars, 5 μm. The graph shows the number of staining cells from three independent experiments. Values represent the mean ± SEM. ∗∗∗ p < 0.001 vs. FSK.

    Article Snippet: • Raw RNA-seq data derived from human placental samples and trophoblast cell lines have been deposited at the DNA DataBank of Japan (DDBJ) Sequence Read Archive as DDBJ: DRA021720 and DRA021721 and are publicly available as of the date of publication.

    Techniques: Expressing, RNA Sequencing, Functional Assay, Western Blot, Control, Binding Assay, Immunofluorescence, Staining