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Endometrial cancer
Blood methods
Endometrial cancerEndometrial cancer begins in the layer of cells that form the inner epithelial lining (endometrium) of the uterus and is sometimes referred to as uterine cancer. There is an increasing incidence worldwide, and it occurs most commonly after menopause with the majority of cases occurring between 65-75 years of age. It is the sixth most commonly occurring cancer in women. Apart from age, other risk factors include high BMI, estrogen exposure, and genetic predisposition, among others. Post-menopausal bleeding is a common symptom of endometrial cancer, and it frequently has overlapping symptoms with ovarian cancer, including pain, constipation, or diarrhea. Chemotherapy is a typical treatment option for most patients with metastatic disease. However, if endometrial cancer is discovered early it can often be cured by removing the uterus surgically (Makker V et al. (2021)) . 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.
Pan-cancer protein panel6 proteins have been selected by the model to predict endometrial cancer (Table 1). The two top proteins for endometrial cancer (PLAT) and (TNFSF10) are both secreted to blood and the origin of tissue expression is relatively heterogeneous, including urothelial and ductal cells, respectively. Both proteins are annotated as related to cancer by UniProt and the latter has been described to be involved in apoptosis (He W et al. (2012)). In an independent study by Enroth et al (Enroth S et al. (2018)), several proteins were upregulated, although in most cases not significant, and four of these (WFDC2, IL-10, ST2 and DKK-4) are also elevated here, although not used by the model. |
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The Human Protein Atlas
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