Q-omics provides the consensus-scored IFNA6 profile across patient tissues and cancer cell-line models. IFNA6 expression is associated with patient survival in 9 of 34 cancer types, with the highest sampling consensus in UCEC. Additionally, IFNA6 protein abundance shows 23,193 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UCEC, and GBM as cancer lineages where IFNA6 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 IFNA6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IFNA6 survival associations across molecular data types. IFNA6 RNA expression shows survival associations in the most cancer types (9), followed by mutation status (2) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IFNA6 RNA expression–survival associations across cancer types. High IFNA6 expression shows unfavorable associations in UCEC, THCA, MESO, COAD, CHOL and LIHC. The UCEC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .014). Together, the overview and detailed table identify UCEC as the clearest survival context for IFNA6 RNA expression.
This table shows molecular features associated with IFNA6 in patient tissues and cancer cell lines. In patient samples, IFNA6 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, IFNA6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BONE and LUNG_SCLC.
