Q-omics provides the consensus-scored IRF8 profile across patient tissues and cancer cell-line models. IRF8 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, IRF8 is differentially expressed in 11, with the highest sampling consensus in LUAD. Additionally, IRF8 RNA expression shows 22,129 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, LUAD, and LSCC as cancer lineages where IRF8 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 IRF8 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IRF8 survival associations across molecular data types. IRF8 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IRF8 RNA expression–survival associations across cancer types. High IRF8 expression shows unfavorable associations in UVM, but favorable associations in SKCM, HNSC, LUAD, KIRC and SARC. 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 IRF8 RNA expression.
This table summarizes IRF8 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for IRF8. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IRF8 shows lower tumor expression in LUAD, KICH, LUSC and LIHC and higher tumor expression in KIRC and STAD. The LUAD box plot shows higher IRF8 RNA expression in normal versus tumor tissue (log2 FC = −1.234, t-test p < 0.001).
This table shows molecular features associated with IRF8 in patient tissues and cancer cell lines. In patient samples, IRF8 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, IRF8 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LUNG_SCLC.
