ImmiR: a database of miRNAs associated with immune checkpoints and immune infiltration.


The Gene section investigates the relationship between immune checkpoint gene expression and associated miRNAs across 32 cancer types, presented from a gene-focused perspective.

Output:

  • A heatmap illustrating correlation strengths between the selected gene and its associated miRNAs across various cancer types.
  • A statistical table detailing correlation coefficients and significance values for each gene–miRNA pair

Instructions:

  1. Input the name of an immune checkpoint gene (e.g., CD276)
  2. Select whether to apply Purity Adjustment (default: enabled)
  3. Click the Submit button to generate results


Example : CD276

Purity Adjustment




Heatmap of miRNA-gene Expression Correlations across Cancer Types

The interactive heatmap displays the correlation patterns between selected immune checkpoint genes and miRNAs across 32 cancer types.

Data Representation:

  • Color-coded cells indicate correlation direction (red: positive, blue: negative, gray: non-significant).
  • Cancer types are displayed along the x-axis —hover over any type to view its full name and tissue origin.
  • The y-axis presents the 30 miRNAs showing correlations with the greatest number of cancer types.

Interactive Elements

Complete information on miRNA validation methods and visualization tools is available in the Help section.



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Association result table

This interactive table displays the statistical relationships between selected immune checkpoint gene and miRNAs across 32 cancer types. For each pair, relevant pathway involvement and supporting evidence are included.
(Note: Only miRNAs associated with more than two cancer types are included.)

Usage Instructions:

  • Use the filter boxes below each column header to narrow results by miRNA, immune checkpoint gene, cancer type, or other fields.
  • Click on any row to view a correlation plot for the selected miRNA–gene pair.
    (Note: If the relationship between variables is non-linear, the correlation coefficient (Rho) may not match the slope of the plotted trend line.)
  • For guidance on customizing or exporting the table, see Help.
Complete information on miRNA validation methods and prediction tools is available in the Help section.




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The miRNA section investigates the relationship between miRNA expression and immune checkpoint genes across 32 cancer types, presented from a miRNA-focused perspective.

Output:

  • An immune checkpoint response barplot showing expression patterns
  • A heatmap illustrating correlation strengths between the selected miRNA and immune checkpoint genes across various cancer types
  • A statistical table detailing correlation coefficients and p-values for miRNA-gene relationships across different cancer types and pathways

Instructions:

  1. Input a miRNA ID (e.g., hsa-miR-152-3p)
  2. Select whether to apply Purity Adjustment (default: enabled)
  3. Click the Submit button to generate results


Example : hsa-miR-152-3p

Purity Adjustment




Barplot of miRNA-Immune Checkpoint Response Correlations across Cancer Types

The interactive barplot displays the immune checkpoint response patterns of the selected miRNA across 32 cancer types, calculated using TIDE (Tumor Immune Dysfunction and Exclusion) methodology.

Data Representation:

  • Y-axis: Correlation coefficients with bidirectional visualization
    • Bar height indicates correlation strength (larger absolute values = stronger correlations)
    • Upward bars: Positive correlations between miRNA expression and immune checkpoint response
    • Downward bars: Negative correlations between miRNA expression and immune checkpoint response
  • X-axis: Cancer types displayed with standardized abbreviations

Interactive Elements

Complete information on miRNA validation methods and visualization tools is available in the Help section.



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Heatmap of miRNA-associated Gene Expression Correlations

across Cancer Types with Pathway Analysis

The interactive heatmap displays the correlation patterns between the selected miRNA and its associated immune checkpoint genes across 32 cancer types, with integrated pathway enrichment visualization.

Data Representation:

  • Main Heatmap:
    • Color-coded cells indicate correlation direction (red: positive, blue: negative, gray: non-significant)
    • Cancer types are displayed along the x-axis —hover over any type to view its full name and tissue origin
    • The y-axis presents immune checkpoint gene names that are associated with the selected miRNA
  • Pathway Panel:
    • Right side displays corresponding biological pathways enriched for the miRNA-associated genes
    • Provides functional context for the correlation patterns observed

Interactive Elements

Complete information on miRNA validation methods and visualization tools is available in the Help section.


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Association result table

This interactive table displays the statistical relationships between selected miRNA and immune checkpoint genes across 32 cancer types. For each pair, relevant pathway involvement and supporting evidence are included. (Note: Only immune checkpoint genes associated with more than one cancer type are included.)

Usage Instructions:

  • Use the filter boxes below each column header to narrow results by miRNA, immune checkpoint gene, cancer type, or other fields.
  • Click on any row to view a correlation plot for the selected miRNA–gene pair.
    (Note: If the relationship between variables is non-linear, the correlation coefficient (Rho) may not match the slope of the plotted trend line.)
  • For guidance on customizing or exporting the table, see Help.
Complete information on miRNA validation methods and prediction tools is available in the Help section.



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The Pahtway section investigates the relationship between immune checkpoint pathways, immune checkpoint genes, and miRNAs across diverse cancer types, presented from a pathway-focused perspective.

Output:

  • A Sankey diagram illustrating the flow and connections between pathway components, miRNAs, and genes across cancer types
  • A statistical table detailing correlation coefficients and p-values for miRNA-gene relationships within specific biological pathways

Instructions:

  1. Select an immune checkpoint pathway from the drop-down list (e.g., 2B4 & CD48 immune checkpoint pathway)
  2. Select whether to apply Purity Adjustment (default: enabled)
  3. Click the Submit button to generate results


Purity Adjustment




Sankey diagram

The interactive Sankey diagram visualizes the multi-layered associations between immune checkpoint pathways, genes, miRNAs, and cancer types, providing a comprehensive view of regulatory networks.

Data Representation:

  • Flow Structure: Four-tier visualization showing connections from:
    • Selected immune checkpoint pathway → Associated genes → Regulatory miRNAs → Cancer types
  • Node Importance: Node size reflects connectivity strength and biological significance
    • Highly connected nodes indicate key regulatory components with broader influence across the immune checkpoint network
  • Flow Width: Connection thickness represents association strength between components

Network Interpretation & Interactions

This visualization enables identification of key regulatory hubs and understanding of how immune checkpoint pathways influence cancer biology through gene-miRNA networks.



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Association result table

This interactive table presents statistical associations between miRNAs and immune checkpoint genes across various cancer types within the selected immune checkpoint pathway. Each row reports the correlation coefficient (Rho), p-value, and q-value for a specific miRNA-gene correlation within each cancer type. The corresponding immune checkpoint pathway and evidence type (predicted or experimentally validated) are also included.

Usage Instructions:

  • Use the filter boxes below each column header to narrow results by miRNA, immune checkpoint gene, cancer type, or other fields.
  • Click on any row to view a correlation plot for the selected miRNA-gene correlation in the corresponding cancer type.
    (Note: If the relationship between variables is non-linear, the correlation coefficient (Rho) may not match the slope of the plotted trend line.)
  • For guidance on customizing or exporting the table, see Help.


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The Cancer section analyzes the relationships among miRNAs, immune checkpoint pathways, and immune checkpoint genes within specific cancer types, presented from a cancer-focused perspective.

Output:

  • A Sankey diagram depicting the relationships between selected cancer type, associated miRNAs, immune checkpoint pathways, and immune checkpoint genes
  • A statistical table presenting correlation statistics for miRNA-gene associations within immune checkpoint pathways for the selected cancer type

Instructions:

  1. Select a tissue type from the drop-down menu (e.g., blood, lung)
  2. Choose a corresponding cancer dataset for the selected tissue type (e.g., DLBC, LUAD)
  3. Select whether to apply Purity Adjustment (default: enabled)
  4. Click the Submit button to generate results


Purity Adjustment




Sankey diagram

The interactive Sankey diagram visualizes the multi-layered associations between cancer types, miRNAs, immune checkpoint pathways, and genes, offering a cancer-focused perspective on regulatory networks.

Data Representation:

  • Flow Structure: Four-tier visualization showing connections from:
    • Selected cancer type → Regulatory miRNAs → Immune checkpoint pathways → Immune checkpoint genes
  • Node Importance: Node size reflects connectivity strength and biological significance
    • Highly connected nodes represent potential regulatory hubs with broader influence within the selected cancer type
  • Flow Width: Connection thickness represents association strength between components

Network Interpretation & Interactions

This visualization aids in identifying critical regulatory interactions and understanding how immune checkpoint pathways are modulated by miRNAs within a particular cancer type.



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Association result table

This interactive table presents statistical associations between miRNAs and immune checkpoint genes within a selected cancer type. Each row reports the correlation coefficient (Rho), p-value, and q-value for a specific miRNA-gene correlation, with results grouped by immune checkpoint pathway for the chosen cancer type.

Usage Instructions:

  • Use the filter boxes below each column header to narrow results by miRNA, immune checkpoint gene, pathway, or evidence type
  • Click on any row to view a correlation plot for the selected miRNA-gene correlation within the corresponding immune checkpoint pathway for the selected cancer type
    (Note: If the relationship between variables is non-linear, the correlation coefficient (Rho) may not match the slope of the plotted trend line.)
  • For guidance on customizing or exporting the table, see Help.



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