GO:1903236Ontology (GO BP)GO biological process · ~18 member genes
Q-omics provides the Regulation of leukocyte tethering or rolling (GO:1903236) pathway profile, scoring each patient from the combined activity of its roughly 18 member genes. Pathway activity is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 10, with the highest sampling consensus in COAD. Additionally, pathway RNA activity shows 30,492 significant cross-omics associations, again with the highest sampling consensus in PRAD. Together, these results highlight HNSC, COAD, and PRAD 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 leukocyte tethering or rolling survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (22). 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 leukocyte tethering or rolling activity shows favorable associations in HNSC, SKCM, LIHC and CESC, but unfavorable associations in STAD and ESCA. In the HNSC Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p < 0.001). HNSC ranks highest by sampling consensus for Regulation of leukocyte tethering or rolling.
This table summarizes Regulation of leukocyte tethering or rolling tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 10 cancer types, while mass-spec protein activity shows differences in 4. The strongest signals are in KIRP 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 consistently lower tumor activity across COAD, KIRP, LUAD, LIHC, LUSC and UCEC. In the COAD box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.077, t-test p < 0.001).
This table shows molecular features associated with Regulation of leukocyte tethering or rolling 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 PRAD. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in BLOOD_Leukemia.
