Q-omics provides the consensus-scored IFNA16 profile across patient tissues and cancer cell-line models. IFNA16 expression is associated with patient survival in 7 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, IFNA16 is differentially expressed in 2, with the highest sampling consensus in KIRC. Additionally, IFNA16 RNA expression shows 7,142 significant gene co-expression associations, with the highest sampling consensus in COAD. Together, these results highlight SCLC, KIRC, and COAD as cancer lineages where IFNA16 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 IFNA16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IFNA16 survival associations across molecular data types. IFNA16 RNA expression shows survival associations in the most cancer types (7), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IFNA16 RNA expression–survival associations across cancer types. High IFNA16 expression shows unfavorable associations in SCLC, UCEC, THYM, LGG, HNSC and STAD. The SCLC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify SCLC as the clearest survival context for IFNA16 RNA expression.
This table summarizes IFNA16 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 2. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for IFNA16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IFNA16 shows lower tumor expression in KIRC and KICH. The KIRC box plot shows higher IFNA16 RNA expression in normal versus tumor tissue (log2 FC = −0.062, t-test p < 0.001).
This table shows molecular features associated with IFNA16 in patient tissues and cancer cell lines. In patient samples, IFNA16 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, IFNA16 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in CNS and LUNG_NSCLC_LUSC.
