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Breast cancerBreast cancerBreast cancer forms in the epithelial cells lining the ducts or lobules of the breast. Breast cancer can occur in both men and women, but is far more common in women. Breast cancer can be non-invasive, which does not go beyond the milk ducts or lobules in the breast, or invasive, which spreads into surrounding tissues and/or distant organs. Symptoms includes a lump or thickening in the breast, abnormal nipple or areola appearance, and abnormal fluid from the nipple. Over time, these in situ cancers may progress and invade the surrounding breast tissue, and subsequently spread throughout the lymph nodes and organs. The stage of breast cancer describes how much the cancer has grown, and if or where it has spread. Breast cancer treatment is varied and can be highly effective, especially when the disease is identified early. Treatment options include radiotherapy, chemotherapy, hormonal therapy, and surgical resection of either the cancerous tissue (lumpectomy) or the whole breast (mastectomy). Risk factors include age, obesity, and family history. Certain gene mutations such as BRCA1, BRCA2, and PALB-2 can increase breast cancer risk. (WHO). Differential abundance and machine learning analysisThis section presents the disease-specific results of the differential abundance and machine learning analyses. The analyses are reported for three comparisons: 1) disease vs. all other diseases, 2) disease vs. diseases from the same class, and 3) disease vs. healthy samples. Disease vs All other
Disease vs Class
Disease vs Healthy
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
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Contact
The Project
The Human Protein Atlas
The Human Protein Atlas project is funded
