GO:1904896Ontology (GO BP)GO biological process · ~10 member genes
Q-omics provides the ESCRT complex disassembly (GO:1904896) pathway profile, scoring each patient from the combined activity of its roughly 10 member genes. Pathway activity is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 13, with the highest sampling consensus in KICH. Additionally, pathway RNA activity shows 36,687 significant cross-omics associations, again with the highest sampling consensus in KIRC. Together, these results highlight MESO, KICH, and KIRC 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 ESCRT complex disassembly survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (24). 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 ESCRT complex disassembly activity shows favorable associations in HNSC and READ, but unfavorable associations in MESO, UCEC, LUAD and SCLC. In the MESO Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p = .004). MESO ranks highest by sampling consensus for ESCRT complex disassembly.
This table summarizes ESCRT complex disassembly tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 13 cancer types, while mass-spec protein activity shows differences in 4. The strongest signals are in KICH for RNA and HNSC 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 BLCA and lower tumor activity in KICH, COAD, THCA, KIRC and LIHC. In the KICH box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.107, t-test p < 0.001).
This table shows molecular features associated with ESCRT complex disassembly 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 KIRC. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in CNS.
