Q-omics provides the consensus-scored IRF2 profile across patient tissues and cancer cell-line models. IRF2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, IRF2 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, IRF2 RNA expression shows 20,049 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight SKCM, KIRC, and ACC as cancer lineages where IRF2 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 IRF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IRF2 survival associations across molecular data types. IRF2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (2) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IRF2 RNA expression–survival associations across cancer types. High IRF2 expression shows unfavorable associations in LGG and ACC, but favorable associations in SKCM, BRCA, UCEC and MESO. The SKCM 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 SKCM as the clearest survival context for IRF2 RNA expression.
This table summarizes IRF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for IRF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IRF2 shows lower tumor expression in LUAD, UCEC and COAD and higher tumor expression in KIRC, HNSC and STAD. The KIRC box plot shows higher IRF2 RNA expression in tumor versus normal tissue (log2 FC = +0.594, t-test p < 0.001).
This table shows molecular features associated with IRF2 in patient tissues and cancer cell lines. In patient samples, IRF2 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, IRF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in CNS and LARGE_INTESTINE.
