GO:0048278Ontology (GO BP)GO biological process · ~64 member genes
Q-omics provides the Vesicle docking (GO:0048278) pathway profile, scoring each patient from the combined activity of its roughly 64 member genes. Pathway activity is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 12, with the highest sampling consensus in THCA. Additionally, pathway RNA activity shows 36,813 significant cross-omics associations, again with the highest sampling consensus in STAD. Together, these results highlight SCLC, THCA, 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 Vesicle docking survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (22). 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 Vesicle docking activity shows favorable associations in SCLC, PAAD, READ and ACC, but unfavorable associations in LIHC and KICH. In the SCLC Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p < 0.001). SCLC ranks highest by sampling consensus for Vesicle docking.
This table summarizes Vesicle docking tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 12 cancer types, while mass-spec protein activity shows differences in 6. The strongest signals are in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal activity differences for the pathway. A positive fold-change indicates higher activity in tumor tissue. The pathway shows higher tumor activity across KIRC, LIHC and KIRP and lower tumor activity in THCA, LUSC and LUAD. In the THCA 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 Vesicle docking 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.
