Q-omics provides the consensus-scored IRF9 profile across patient tissues and cancer cell-line models. IRF9 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, IRF9 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, IRF9 RNA expression shows 19,308 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight SKCM, HNSC, and UVM as cancer lineages where IRF9 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 IRF9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IRF9 survival associations across molecular data types. IRF9 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (4) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IRF9 RNA expression–survival associations across cancer types. High IRF9 expression shows unfavorable associations in ACC, KIRC and UVM, but favorable associations in SKCM, BLCA and BRCA. 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 IRF9 RNA expression.
This table summarizes IRF9 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 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for IRF9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IRF9 shows lower tumor expression in KICH and higher tumor expression in HNSC, BLCA, STAD, KIRC and BRCA. The HNSC box plot shows higher IRF9 RNA expression in tumor versus normal tissue (log2 FC = +1.049, t-test p < 0.001).
This table shows molecular features associated with IRF9 in patient tissues and cancer cell lines. In patient samples, IRF9 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, IRF9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and LARGE_INTESTINE.
