GO:0033212Ontology (GO BP)GO biological process · ~10 member genes
Q-omics provides the Iron import into cell (GO:0033212) pathway profile, scoring each patient from the combined activity of its roughly 10 member genes. Pathway activity is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 11, with the highest sampling consensus in LIHC. Additionally, pathway RNA activity shows 34,613 significant cross-omics associations, again with the highest sampling consensus in BRCA. Together, these results highlight CESC, LIHC, and BRCA 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 Iron import into cell survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (25). 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 Iron import into cell activity shows favorable associations in ACC, MESO and BRCA, but unfavorable associations in CESC, SKCM and UCS. In the CESC Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). CESC ranks highest by sampling consensus for Iron import into cell.
This table summarizes Iron import into cell tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 11 cancer types, while mass-spec protein activity shows differences in 3. The strongest signals are in KIRC for RNA and LSCC 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 LIHC, KIRC, KIRP, BRCA, LUSC and KICH. In the LIHC box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.093, t-test p < 0.001).
This table shows molecular features associated with Iron import into cell 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 BRCA. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in PANCREAS.
