GO:0018200Ontology (GO BP)GO biological process · ~26 member genes
Q-omics provides the Peptidyl-glutamic acid modification (GO:0018200) pathway profile, scoring each patient from the combined activity of its roughly 26 member genes. Pathway activity is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 9, with the highest sampling consensus in LIHC. Additionally, pathway RNA activity shows 34,180 significant cross-omics associations, again with the highest sampling consensus in KIRP. Together, these results highlight ACC, LIHC, and KIRP 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 Peptidyl-glutamic acid modification survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (24). 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 Peptidyl-glutamic acid modification activity shows favorable associations in OV, but unfavorable associations in ACC, KIRC, BLCA, LIHC and KICH. In the ACC Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). ACC ranks highest by sampling consensus for Peptidyl-glutamic acid modification.
This table summarizes Peptidyl-glutamic acid modification tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 9 cancer types, while mass-spec protein activity shows differences in 2. The strongest signals are in LIHC 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 LIHC, KIRP and COAD and lower tumor activity in BRCA, UCEC and ESCA. In the LIHC box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.047, t-test p < 0.001).
This table shows molecular features associated with Peptidyl-glutamic acid modification 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 KIRP. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in PANCREAS.
