GO:0060736Ontology (GO BP)GO biological process · ~11 member genes
Q-omics provides the Prostate gland growth (GO:0060736) pathway profile, scoring each patient from the combined activity of its roughly 11 member genes. Pathway activity is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. 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 34,729 significant cross-omics associations, again with the highest sampling consensus in BRCA. Together, these results highlight ACC, KICH, and BRCA 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 Prostate gland growth 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 Prostate gland growth activity shows favorable associations in ACC, BRCA, KIRC, COAD and KIRP, but unfavorable associations in SKCM. In the ACC Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p < 0.001). ACC ranks highest by sampling consensus for Prostate gland growth.
This table summarizes Prostate gland growth 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 2. The strongest signals are in KICH for RNA and COAD 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 KICH, LUSC and PAAD and lower tumor activity in LIHC, UCEC and HNSC. In the KICH box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.095, t-test p < 0.001).
This table shows molecular features associated with Prostate gland growth 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 BRCA. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in SKIN.
