GO:0010001Ontology (GO BP)GO biological process · ~249 member genes
Q-omics provides the Glial cell differentiation (GO:0010001) pathway profile, scoring each patient from the combined activity of its roughly 249 member genes. Pathway activity is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 12, with the highest sampling consensus in KICH. Additionally, pathway RNA activity shows 36,893 significant cross-omics associations, again with the highest sampling consensus in STAD. Together, these results highlight HNSC, KICH, and STAD as cancer lineages where the pathway shows reproducible signals across outcome, tissue activity, and molecular association analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns. Pathway-against-pathway and pathway-against-mutation comparisons are not available for ontology entities.
Survival associations
This table summarizes Glial cell differentiation survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (23). The rightmost column indicates the cancer type with the highest sampling consensus for each layer.
This table ranks reproducible pathway activity–survival associations across cancer types. High Glial cell differentiation activity shows favorable associations in HNSC, KIRP and SKCM, but unfavorable associations in THCA, KIRC and LIHC. In the HNSC Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p < 0.001). HNSC ranks highest by sampling consensus for Glial cell differentiation.
This table summarizes Glial cell differentiation tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 12 cancer types. The strongest signals are in KICH for RNA.
This table ranks reproducible tumor–normal activity differences for the pathway. A positive fold-change indicates higher activity in tumor tissue. The pathway shows consistently lower tumor activity across KICH, LUSC, LUAD, BLCA, KIRC and BRCA. In the KICH box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.057, t-test p < 0.001).
This table shows molecular features associated with Glial cell differentiation pathway activity in patient tissues and cancer cell lines. In patient samples, pathway activity is most strongly linked to RNA and protein features, with the largest associated set in STAD. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in SKIN.
