GO:0090324Ontology (GO BP)GO biological process · ~8 member genes
Q-omics provides the Negative regulation of oxidative phosphorylation (GO:0090324) pathway profile, scoring each patient from the combined activity of its roughly 8 member genes. Pathway activity is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 11, with the highest sampling consensus in COAD. Additionally, pathway RNA activity shows 32,586 significant cross-omics associations, again with the highest sampling consensus in KIRC. Together, these results highlight SKCM, 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 Negative regulation of oxidative phosphorylation survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (20). 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 Negative regulation of oxidative phosphorylation activity shows favorable associations in THCA and KIRP, but unfavorable associations in SKCM, LUAD, KIRC and ESCA. In the SKCM Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). SKCM ranks highest by sampling consensus for Negative regulation of oxidative phosphorylation.
This table summarizes Negative regulation of oxidative phosphorylation tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 11 cancer types, while mass-spec protein activity shows differences in 3. 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 higher tumor activity across COAD, THCA, LUAD and STAD and lower tumor activity in KIRP and CHOL. In the COAD box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.104, t-test p < 0.001).
This table shows molecular features associated with Negative regulation of oxidative phosphorylation 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.
