GO:0060046Ontology (GO BP)GO biological process · ~17 member genes
Q-omics provides the Regulation of acrosome reaction (GO:0060046) pathway profile, scoring each patient from the combined activity of its roughly 17 member genes. Pathway activity is associated with patient survival in 27 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 7, with the highest sampling consensus in LUAD. Additionally, pathway RNA activity shows 29,475 significant cross-omics associations, again with the highest sampling consensus in KIRC. Together, these results highlight SCLC, LUAD, 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 Regulation of acrosome reaction survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (27). 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 acrosome reaction activity shows favorable associations in BLCA, but unfavorable associations in SCLC, GBM, KIRP, KIRC and UVM. In the SCLC Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). SCLC ranks highest by sampling consensus for Regulation of acrosome reaction.
This table summarizes Regulation of acrosome reaction tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 7 cancer types, while mass-spec protein activity shows differences in 4. The strongest signals are in LUAD 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 LUAD, BRCA, LUSC and HNSC and lower tumor activity in STAD and READ. In the LUAD box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.100, t-test p < 0.001).
This table shows molecular features associated with Regulation of acrosome reaction 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 BLOOD_Myeloma.
