GO:0045670Ontology (GO BP)GO biological process · ~69 member genes
Q-omics provides the Regulation of osteoclast differentiation (GO:0045670) pathway profile, scoring each patient from the combined activity of its roughly 69 member genes. Pathway activity is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in LUSC. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 12, with the highest sampling consensus in KIRC. Additionally, pathway RNA activity shows 35,318 significant cross-omics associations, again with the highest sampling consensus in STAD. Together, these results highlight LUSC, KIRC, 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 osteoclast differentiation 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 Regulation of osteoclast differentiation activity shows favorable associations in SKCM, HNSC and UCS, but unfavorable associations in LUSC, LGG and GBM. In the LUSC Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). LUSC ranks highest by sampling consensus for Regulation of osteoclast differentiation.
This table summarizes Regulation of osteoclast differentiation tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 12 cancer types, while mass-spec protein activity shows differences in 6. The strongest signals are in KIRC 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 KIRC and lower tumor activity in LUSC, LUAD, BRCA, LIHC and THCA. In the KIRC box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.027, t-test p < 0.001).
This table shows molecular features associated with Regulation of osteoclast differentiation 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 UPPER_AERODIGESTIVE_TRACT.
