GO:0048821Ontology (GO BP)GO biological process · ~40 member genes
Q-omics provides the Erythrocyte development (GO:0048821) pathway profile, scoring each patient from the combined activity of its roughly 40 member genes. Pathway activity is associated with patient survival in 24 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 14, with the highest sampling consensus in LUAD. Additionally, pathway RNA activity shows 36,666 significant cross-omics associations, again with the highest sampling consensus in HNSC. Together, these results highlight SKCM, 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 Erythrocyte development 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 Erythrocyte development activity shows favorable associations in SKCM and HNSC, but unfavorable associations in UVM, KIRC, KIRP and THCA. In the SKCM Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p < 0.001). SKCM ranks highest by sampling consensus for Erythrocyte development.
This table summarizes Erythrocyte development 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 4. The strongest signals are in LUAD 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 LUAD, STAD, BLCA, COAD and LUSC and lower tumor activity in KICH. In the LUAD box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.062, t-test p < 0.001).
This table shows molecular features associated with Erythrocyte development 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.
