GO:0022401Ontology (GO BP)GO biological process · ~21 member genes
Q-omics provides the Negative adaptation of signaling pathway (GO:0022401) pathway profile, scoring each patient from the combined activity of its roughly 21 member genes. Pathway activity is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in BRCA. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 13, with the highest sampling consensus in LUAD. Additionally, pathway RNA activity shows 28,671 significant cross-omics associations, again with the highest sampling consensus in HNSC. Together, these results highlight BRCA, LUAD, and HNSC 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 adaptation of signaling pathway survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (26). 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 adaptation of signaling pathway activity shows favorable associations in LGG and SARC, but unfavorable associations in BRCA, ESCA, KIRP and SKCM. In the BRCA Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). BRCA ranks highest by sampling consensus for Negative adaptation of signaling pathway.
This table summarizes Negative adaptation of signaling pathway 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 6. The strongest signals are in KIRC for RNA and PDAC 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, COAD and KICH and lower tumor activity in LUAD, LUSC and BRCA. In the LUAD box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.181, t-test p < 0.001).
This table shows molecular features associated with Negative adaptation of signaling pathway 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 HNSC. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in SKIN.
