Q-omics provides the consensus-scored ICA1 profile across patient tissues and cancer cell-line models. ICA1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ICA1 is differentially expressed in 16, with the highest sampling consensus in KIRC. Additionally, ICA1 protein abundance shows 33,527 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, and GBM as cancer lineages where ICA1 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics 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.
Premium analyses for ICA1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ICA1 survival associations across molecular data types. ICA1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ICA1 RNA expression–survival associations across cancer types. High ICA1 expression shows unfavorable associations in UVM, CESC and UCS, but favorable associations in KIRC, HNSC and UCEC. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for ICA1 RNA expression.
This table summarizes ICA1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 11. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ICA1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ICA1 shows lower tumor expression in KIRC, HNSC and LUSC and higher tumor expression in COAD, LUAD and STAD. The KIRC box plot shows higher ICA1 RNA expression in normal versus tumor tissue (log2 FC = −1.555, t-test p < 0.001).
This table shows molecular features associated with ICA1 in patient tissues and cancer cell lines. In patient samples, ICA1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, ICA1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
