GO:1903533Ontology (GO BP)GO biological process · ~79 member genes
Q-omics provides the Regulation of protein targeting (GO:1903533) pathway profile, scoring each patient from the combined activity of its roughly 79 member genes. Pathway activity is associated with patient survival in 24 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 10, with the highest sampling consensus in KICH. Additionally, pathway RNA activity shows 36,827 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 Regulation of protein targeting 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 Regulation of protein targeting activity shows favorable associations in HNSC, SKCM, ESCA and BRCA, but unfavorable associations in KIRP and GBM. In the HNSC Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p = .001). HNSC ranks highest by sampling consensus for Regulation of protein targeting.
This table summarizes Regulation of protein targeting tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 10 cancer types, while mass-spec protein activity shows differences in 3. The strongest signals are in KICH for RNA and LSCC 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 LIHC and CHOL and lower tumor activity in KICH, LUAD, LUSC and BRCA. In the KICH box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.056, t-test p < 0.001).
This table shows molecular features associated with Regulation of protein targeting 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 BLOOD_Leukemia.
