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Human Protein Atlas rna expression data
Rna Expression Data, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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The expression patterns of <t>SELPLG</t> across different cancer types and clinical stages based on analyses conducted using TIMER2 and other computational tools. A Expression analysis of SELPLG across various cancers with or without paracancer generated from TIMER ( https://cistrome.shinyapps.io/timer/ ). B Expression analysis of SELPLG across various cancers with or without paracancer generated from other analyses. C Analysis of SELPLG expression across TNM stage samples. D Differential expression analysis of SELPLG across clinical T stage samples

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(A) Heatmap of differentially expressed genes <t>from</t> <t>RNA-sequencing</t> analysis of A549 cells with stable RHOV knockdown (sh1, sh2) compared to control. Consistent transcriptional alterations across both shRNAs are observed, with enrichment of MYC-related gene signatures among downregulated transcripts. (B) Gene ontology (GO) of differentially expressed genes shows significant downregulation of pathways associated with RNA biosynthesis, mRNA catabolism, ribosomal assembly, and mitotic cell cycle—all known MYC-driven processes. Upregulated pathways include DNA damage response, chromatin organization, and endocytic trafficking. Enrichment scores are represented along with q-values. (C) Gene Set Enrichment Analysis (GSEA) using Hallmark gene sets confirms strong negative enrichment of hallmark MYC target gene sets (V1 and V2) and oxidative phosphorylation genes in RHOV knockdown cells, indicating suppression of both proliferative and metabolic MYC programs. (D) Expression of MYC was compared between high and Low RHOV expressing patients in four NSCLC patients’ datasets. The expression of MYC was significantly higher in high RHOV patients (Mann-Whitney test). (E, F and G) The patients’ samples with high expression of RHOV (which show poor survival, ) from three cohort, (E) EAC, (F) Microarray and (G) TCGA were divided into two groups based on MYC expression. The analysis shows that RHOV high patients with low MYC expression survive significantly better than the RHOV high patients with high MYC expression. The p-values and Hazard ration are shown.

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The expression patterns of SELPLG across different cancer types and clinical stages based on analyses conducted using TIMER2 and other computational tools. A Expression analysis of SELPLG across various cancers with or without paracancer generated from TIMER ( https://cistrome.shinyapps.io/timer/ ). B Expression analysis of SELPLG across various cancers with or without paracancer generated from other analyses. C Analysis of SELPLG expression across TNM stage samples. D Differential expression analysis of SELPLG across clinical T stage samples

Journal: Discover Oncology

Article Title: Comprehensive analysis of SELPLG as a potential immunotherapy target and prognostic biomarker in oncology

doi: 10.1007/s12672-025-02934-0

Figure Lengend Snippet: The expression patterns of SELPLG across different cancer types and clinical stages based on analyses conducted using TIMER2 and other computational tools. A Expression analysis of SELPLG across various cancers with or without paracancer generated from TIMER ( https://cistrome.shinyapps.io/timer/ ). B Expression analysis of SELPLG across various cancers with or without paracancer generated from other analyses. C Analysis of SELPLG expression across TNM stage samples. D Differential expression analysis of SELPLG across clinical T stage samples

Article Snippet: SELPLG RNA expression across various cell types was profiled using data from the Human Protein Atlas (HPA).

Techniques: Expressing, Generated, Quantitative Proteomics

Comprehensive analysis of SELPLG expression and subcellular localization across various cell types and conditions. A SELPLG expression profiles in diverse tumor cell lines, as derived from The Human Protein Atlas. This panel displays differential expression levels across multiple tumor-derived cell lines. B Schematic representation of SELPLG subcellular localization, highlighting its predominant distribution within key cellular compartments. C Immunofluorescence staining of SELPLG localization in HEL, JURKAT, and U2OS cell lines. Images obtained from The Human Protein Atlas illustrate specific subcellular structures where SELPLG is detected. D Relative SELPLG expression across various cell types, including endothelial cells, smooth muscle cells, fibroblasts, and macrophages. E Comparative enrichment of SELPLG expression among core cell populations, indicating the cell types with the highest and lowest levels of expression. F Single-cell RNA sequencing analysis of SELPLG expression across immune cell subsets. Expression variability is shown across T cells, plasma cells, and other immune cell types

Journal: Discover Oncology

Article Title: Comprehensive analysis of SELPLG as a potential immunotherapy target and prognostic biomarker in oncology

doi: 10.1007/s12672-025-02934-0

Figure Lengend Snippet: Comprehensive analysis of SELPLG expression and subcellular localization across various cell types and conditions. A SELPLG expression profiles in diverse tumor cell lines, as derived from The Human Protein Atlas. This panel displays differential expression levels across multiple tumor-derived cell lines. B Schematic representation of SELPLG subcellular localization, highlighting its predominant distribution within key cellular compartments. C Immunofluorescence staining of SELPLG localization in HEL, JURKAT, and U2OS cell lines. Images obtained from The Human Protein Atlas illustrate specific subcellular structures where SELPLG is detected. D Relative SELPLG expression across various cell types, including endothelial cells, smooth muscle cells, fibroblasts, and macrophages. E Comparative enrichment of SELPLG expression among core cell populations, indicating the cell types with the highest and lowest levels of expression. F Single-cell RNA sequencing analysis of SELPLG expression across immune cell subsets. Expression variability is shown across T cells, plasma cells, and other immune cell types

Article Snippet: SELPLG RNA expression across various cell types was profiled using data from the Human Protein Atlas (HPA).

Techniques: Expressing, Derivative Assay, Quantitative Proteomics, Immunofluorescence, Staining, RNA Sequencing, Clinical Proteomics

Comprehensive analysis of the mutational landscape associated with SELPLG across different cancer types. A Pan-cancer analysis of SELPLG gene mutation types in various types of cancers. The mutation types and mutant genes of the high- and low-expression groups of SELPLG in STAD ( B ) and COAD ( C ) were analyzed. D Correlation of SELPLG expression with TMB

Journal: Discover Oncology

Article Title: Comprehensive analysis of SELPLG as a potential immunotherapy target and prognostic biomarker in oncology

doi: 10.1007/s12672-025-02934-0

Figure Lengend Snippet: Comprehensive analysis of the mutational landscape associated with SELPLG across different cancer types. A Pan-cancer analysis of SELPLG gene mutation types in various types of cancers. The mutation types and mutant genes of the high- and low-expression groups of SELPLG in STAD ( B ) and COAD ( C ) were analyzed. D Correlation of SELPLG expression with TMB

Article Snippet: SELPLG RNA expression across various cell types was profiled using data from the Human Protein Atlas (HPA).

Techniques: Mutagenesis, Expressing

Univariate Cox regression analyses assessing the prognostic impact of SELPLG expression across different types of cancer. A The association between SELPLG expression levels and overall survival (OS) rates across various cancer types using the Cox regression model. B The univariate Cox regression analyses evaluating SELPLG in terms of disease-specific survival (DSS) rates across 33 types of cancer in the TCGA database. C The outcomes of univariate Cox regression analyses examining SELPLG in relation to disease-free interval (DFI) rates in diverse cancer types. D The results of univariate Cox regression analyses assessing SELPLG for progression-free interval (PFI) rates across various cancer types

Journal: Discover Oncology

Article Title: Comprehensive analysis of SELPLG as a potential immunotherapy target and prognostic biomarker in oncology

doi: 10.1007/s12672-025-02934-0

Figure Lengend Snippet: Univariate Cox regression analyses assessing the prognostic impact of SELPLG expression across different types of cancer. A The association between SELPLG expression levels and overall survival (OS) rates across various cancer types using the Cox regression model. B The univariate Cox regression analyses evaluating SELPLG in terms of disease-specific survival (DSS) rates across 33 types of cancer in the TCGA database. C The outcomes of univariate Cox regression analyses examining SELPLG in relation to disease-free interval (DFI) rates in diverse cancer types. D The results of univariate Cox regression analyses assessing SELPLG for progression-free interval (PFI) rates across various cancer types

Article Snippet: SELPLG RNA expression across various cell types was profiled using data from the Human Protein Atlas (HPA).

Techniques: Expressing

Gene Set Enrichment Analysis (GSEA) of hallmark pathways associated with SELPLG expression in pan-cancer cohorts. A Gene ontology (GO) enrichment analysis of SELPLG with immune-related functions. This panel displays the results of GO enrichment analysis, highlighting the biological processes, molecular functions, and cellular components associated with SELPLG expression, particularly focusing on immune-related functions. The analysis was performed using the GSEA method on pan-cancer data. B , D Correlations of SELPLG with immune-related pathways revealed by KEGG pathway analysis. These panels illustrate the significant immune-related pathways correlated with SELPLG expression, as identified through KEGG pathway analysis. C Hallmark pathway enrichment analysis of SELPLG ’s involvement in immune-related processes. The analysis was conducted using the GSEA method, and the results are visualized to show the normalized enrichment scores (NES) and false discovery rates (FDR) for each pathway

Journal: Discover Oncology

Article Title: Comprehensive analysis of SELPLG as a potential immunotherapy target and prognostic biomarker in oncology

doi: 10.1007/s12672-025-02934-0

Figure Lengend Snippet: Gene Set Enrichment Analysis (GSEA) of hallmark pathways associated with SELPLG expression in pan-cancer cohorts. A Gene ontology (GO) enrichment analysis of SELPLG with immune-related functions. This panel displays the results of GO enrichment analysis, highlighting the biological processes, molecular functions, and cellular components associated with SELPLG expression, particularly focusing on immune-related functions. The analysis was performed using the GSEA method on pan-cancer data. B , D Correlations of SELPLG with immune-related pathways revealed by KEGG pathway analysis. These panels illustrate the significant immune-related pathways correlated with SELPLG expression, as identified through KEGG pathway analysis. C Hallmark pathway enrichment analysis of SELPLG ’s involvement in immune-related processes. The analysis was conducted using the GSEA method, and the results are visualized to show the normalized enrichment scores (NES) and false discovery rates (FDR) for each pathway

Article Snippet: SELPLG RNA expression across various cell types was profiled using data from the Human Protein Atlas (HPA).

Techniques: Expressing

Association between SELPLG expression and immune cell infiltration levels across TCGA tumors based on TIMER2.0 and CIBERSORT analyses. A The correlation between SELPLG and infiltration level of CD8 + T cells using TIMER2 database. B The correlation between SELPLG and infiltration level of B cells using TIMER2 database. C The correlation between SELPLG and infiltration level of CD4 + T cells using TIMER2 database. D The correlation between SELPLG and infiltration level of Macrophage using TIMER2 database. E The correlation between SELPLG and infiltration level of MDSC using TIMER2 database. F The correlation between SELPLG and infiltration level of Tregs using TIMER2 database. G The correlation between SELPLG and infiltration level of indicated immune cells using data from CIBERSOFT database. *p < 0.05; **p < 0.01; ***p < 0.001

Journal: Discover Oncology

Article Title: Comprehensive analysis of SELPLG as a potential immunotherapy target and prognostic biomarker in oncology

doi: 10.1007/s12672-025-02934-0

Figure Lengend Snippet: Association between SELPLG expression and immune cell infiltration levels across TCGA tumors based on TIMER2.0 and CIBERSORT analyses. A The correlation between SELPLG and infiltration level of CD8 + T cells using TIMER2 database. B The correlation between SELPLG and infiltration level of B cells using TIMER2 database. C The correlation between SELPLG and infiltration level of CD4 + T cells using TIMER2 database. D The correlation between SELPLG and infiltration level of Macrophage using TIMER2 database. E The correlation between SELPLG and infiltration level of MDSC using TIMER2 database. F The correlation between SELPLG and infiltration level of Tregs using TIMER2 database. G The correlation between SELPLG and infiltration level of indicated immune cells using data from CIBERSOFT database. *p < 0.05; **p < 0.01; ***p < 0.001

Article Snippet: SELPLG RNA expression across various cell types was profiled using data from the Human Protein Atlas (HPA).

Techniques: Expressing

The correlations between SELPLG expression and 122 immunomodulators, including chemokines, chemokine receptors, MHC molecules, immunoinhibitors, and immunostimulators. A Heatmap representation of SELPLG correlations with chemokine and MHC genes. B Heatmap representation of SELPLG correlations with chemokine receptor genes. C SELPLG ’s correlations with immunoinhibitor genes are depicted in a heatmap, underscoring its interactions with key regulators of immune response modulation. D Heatmap representation of SELPLG correlations with immunostimulator genes

Journal: Discover Oncology

Article Title: Comprehensive analysis of SELPLG as a potential immunotherapy target and prognostic biomarker in oncology

doi: 10.1007/s12672-025-02934-0

Figure Lengend Snippet: The correlations between SELPLG expression and 122 immunomodulators, including chemokines, chemokine receptors, MHC molecules, immunoinhibitors, and immunostimulators. A Heatmap representation of SELPLG correlations with chemokine and MHC genes. B Heatmap representation of SELPLG correlations with chemokine receptor genes. C SELPLG ’s correlations with immunoinhibitor genes are depicted in a heatmap, underscoring its interactions with key regulators of immune response modulation. D Heatmap representation of SELPLG correlations with immunostimulator genes

Article Snippet: SELPLG RNA expression across various cell types was profiled using data from the Human Protein Atlas (HPA).

Techniques: Expressing

(A) Heatmap of differentially expressed genes from RNA-sequencing analysis of A549 cells with stable RHOV knockdown (sh1, sh2) compared to control. Consistent transcriptional alterations across both shRNAs are observed, with enrichment of MYC-related gene signatures among downregulated transcripts. (B) Gene ontology (GO) of differentially expressed genes shows significant downregulation of pathways associated with RNA biosynthesis, mRNA catabolism, ribosomal assembly, and mitotic cell cycle—all known MYC-driven processes. Upregulated pathways include DNA damage response, chromatin organization, and endocytic trafficking. Enrichment scores are represented along with q-values. (C) Gene Set Enrichment Analysis (GSEA) using Hallmark gene sets confirms strong negative enrichment of hallmark MYC target gene sets (V1 and V2) and oxidative phosphorylation genes in RHOV knockdown cells, indicating suppression of both proliferative and metabolic MYC programs. (D) Expression of MYC was compared between high and Low RHOV expressing patients in four NSCLC patients’ datasets. The expression of MYC was significantly higher in high RHOV patients (Mann-Whitney test). (E, F and G) The patients’ samples with high expression of RHOV (which show poor survival, ) from three cohort, (E) EAC, (F) Microarray and (G) TCGA were divided into two groups based on MYC expression. The analysis shows that RHOV high patients with low MYC expression survive significantly better than the RHOV high patients with high MYC expression. The p-values and Hazard ration are shown.

Journal: bioRxiv

Article Title: TGF/J Regulated Small GTPase RHOV interact with PEAK1 and drive MYC Expression to Promote Cellular Proliferation, Migration and Etoposide resistance

doi: 10.1101/2025.04.18.649622

Figure Lengend Snippet: (A) Heatmap of differentially expressed genes from RNA-sequencing analysis of A549 cells with stable RHOV knockdown (sh1, sh2) compared to control. Consistent transcriptional alterations across both shRNAs are observed, with enrichment of MYC-related gene signatures among downregulated transcripts. (B) Gene ontology (GO) of differentially expressed genes shows significant downregulation of pathways associated with RNA biosynthesis, mRNA catabolism, ribosomal assembly, and mitotic cell cycle—all known MYC-driven processes. Upregulated pathways include DNA damage response, chromatin organization, and endocytic trafficking. Enrichment scores are represented along with q-values. (C) Gene Set Enrichment Analysis (GSEA) using Hallmark gene sets confirms strong negative enrichment of hallmark MYC target gene sets (V1 and V2) and oxidative phosphorylation genes in RHOV knockdown cells, indicating suppression of both proliferative and metabolic MYC programs. (D) Expression of MYC was compared between high and Low RHOV expressing patients in four NSCLC patients’ datasets. The expression of MYC was significantly higher in high RHOV patients (Mann-Whitney test). (E, F and G) The patients’ samples with high expression of RHOV (which show poor survival, ) from three cohort, (E) EAC, (F) Microarray and (G) TCGA were divided into two groups based on MYC expression. The analysis shows that RHOV high patients with low MYC expression survive significantly better than the RHOV high patients with high MYC expression. The p-values and Hazard ration are shown.

Article Snippet: RNA-sequencing (RNA-seq) expression data from The Cancer Genome Atlas (TCGA) project were downloaded from the Broad Institute GDAC Firehose platform ( https://gdac.broadinstitute.org ).

Techniques: RNA Sequencing, Knockdown, Control, Phospho-proteomics, Expressing, MANN-WHITNEY, Microarray