Q-omics provides the consensus-scored IFNA2 profile across patient tissues and cancer cell-line models. IFNA2 expression is associated with patient survival in 10 of 34 cancer types, with the highest sampling consensus in HNSC. Additionally, IFNA2 RNA expression shows 7,085 significant gene co-expression associations, with the highest sampling consensus in COAD. Together, these results highlight HNSC, and COAD as cancer lineages where IFNA2 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 IFNA2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IFNA2 survival associations across molecular data types. IFNA2 RNA expression shows survival associations in the most cancer types (10), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IFNA2 RNA expression–survival associations across cancer types. High IFNA2 expression shows unfavorable associations in UCS, THYM, STAD, UCEC and SKCM, but favorable associations in HNSC. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .003). Together, the overview and detailed table identify HNSC as the clearest survival context for IFNA2 RNA expression.
This table shows molecular features associated with IFNA2 in patient tissues and cancer cell lines. In patient samples, IFNA2 shows the broadest associations at the RNA and protein expression levels, with COAD recurring as the lineage with the largest associated feature set. In cancer cell lines, IFNA2 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.
