GO:0042303Ontology (GO BP)GO biological process · ~116 member genes
Q-omics provides the Molting cycle (GO:0042303) pathway profile, scoring each patient from the combined activity of its roughly 116 member genes. Pathway activity is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 14, with the highest sampling consensus in LIHC. Additionally, pathway RNA activity shows 36,571 significant cross-omics associations, again with the highest sampling consensus in STAD. Together, these results highlight MESO, LIHC, 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 Molting cycle 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 Molting cycle activity shows favorable associations in HNSC, but unfavorable associations in MESO, LGG, DLBC, LUAD and STAD. In the MESO Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p = .001). MESO ranks highest by sampling consensus for Molting cycle.
This table summarizes Molting cycle tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 14 cancer types, while mass-spec protein activity shows differences in 6. The strongest signals are in LIHC for RNA and LUAD 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 LIHC, LUSC, HNSC and CHOL and lower tumor activity in BRCA and UCEC. In the LIHC box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.020, t-test p < 0.001).
This table shows molecular features associated with Molting cycle 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 BLOOD_Myeloma.
