Q-omics provides the consensus-scored IFNA13 profile across patient tissues and cancer cell-line models. IFNA13 expression is associated with patient survival in 12 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, IFNA13 is differentially expressed in 1, with the highest sampling consensus in HNSC. Additionally, IFNA13 RNA expression shows 6,454 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UCEC, HNSC, and GBM as cancer lineages where IFNA13 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 IFNA13 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IFNA13 survival associations across molecular data types. IFNA13 RNA expression shows survival associations in the most cancer types (12), followed by mutation status (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IFNA13 RNA expression–survival associations across cancer types. High IFNA13 expression shows unfavorable associations in UCEC, LUAD, BRCA, READ, ACC and LUSC. The UCEC 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 UCEC as the clearest survival context for IFNA13 RNA expression.
This table summarizes IFNA13 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 1. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for IFNA13. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IFNA13 shows higher tumor expression in HNSC. The HNSC box plot shows higher IFNA13 RNA expression in tumor versus normal tissue (log2 FC = +0.015, t-test p = .042).
This table shows molecular features associated with IFNA13 in patient tissues and cancer cell lines. In patient samples, IFNA13 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, IFNA13 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and LUNG_NSCLC_LUAD.
