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Cervical cancer Cervical cancerCervical cancer begins when healthy cells on the surface of the cervix change or become infected with certain types of human papillomavirus (HPV) and grow out of control to become a tumor. HPV causes more than 90% of all cases of cervical cancer and a long-term infection of HPV on the cervix can result in a cancerous tumor that can be malignant and spread to other parts of the body. Cervical cancer typically takes over 10 to 20 years to develop from precancerous changes. The two main types of cervical cancers are squamous cell carcinoma (80% to 90%), which starts in the cells on the outer surface covering of the cervix, and adenocarcinoma (10% to 20%), which starts in the glandular cells that line the lower birth canal in the internal portion of the cervix. Cervical cancer is both the fourth-most common type of cancer and the fourth-most common cause of death from cancer in women worldwide. Differential Abundance Analysis ResultsThis section presents the results of the differential protein abundance analysis, visualized through a volcano plot and summarized in the accompanying table for all three comparisons: 1) disease vs. healthy samples, 2) disease vs. diseases from the same class, and 3) disease vs. all other diseases. Disease vs Healthy
Disease vs Class
Disease vs All other
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.
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.
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.
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.
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.
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.
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Contact
The Project
The Human Protein Atlas
The Human Protein Atlas project is funded
