Q-omics provides the consensus-scored AIF1L profile across patient tissues and cancer cell-line models. AIF1L expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, AIF1L is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, AIF1L RNA expression shows 18,448 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight ACC, KIRC, and KIRP as cancer lineages where AIF1L 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 AIF1L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AIF1L survival associations across molecular data types. AIF1L RNA expression shows survival associations in the most cancer types (25), followed by mutation status (1) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AIF1L RNA expression–survival associations across cancer types. High AIF1L expression shows unfavorable associations in ACC, MESO, COAD and UCEC, but favorable associations in KIRP and KIRC. The ACC 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 ACC as the clearest survival context for AIF1L RNA expression.
This table summarizes AIF1L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AIF1L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AIF1L shows lower tumor expression in KIRC, KICH, THCA, KIRP, HNSC and LUAD. The KIRC box plot shows higher AIF1L RNA expression in normal versus tumor tissue (log2 FC = −3.919, t-test p < 0.001).
This table shows molecular features associated with AIF1L in patient tissues and cancer cell lines. In patient samples, AIF1L shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, AIF1L RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and SOFT_TISSUE.
