GO:0060134Ontology (GO BP)GO biological process · ~13 member genes
Q-omics provides the Prepulse inhibition (GO:0060134) pathway profile, scoring each patient from the combined activity of its roughly 13 member genes. Pathway activity is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 10, with the highest sampling consensus in COAD. Additionally, pathway RNA activity shows 29,627 significant cross-omics associations, again with the highest sampling consensus in KIRC. Together, these results highlight BLCA, COAD, 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 Prepulse inhibition survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (21). 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 Prepulse inhibition activity shows favorable associations in KIRC, UCEC, LGG, UVM and LUSC, but unfavorable associations in BLCA. In the BLCA Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). BLCA ranks highest by sampling consensus for Prepulse inhibition.
This table summarizes Prepulse inhibition 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 4. The strongest signals are in COAD 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 consistently higher tumor activity across COAD, THCA, LIHC, CHOL, PRAD and KIRP. In the COAD box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.129, t-test p < 0.001).
This table shows molecular features associated with Prepulse inhibition 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 BREAST.
