Q-omics provides the consensus-scored AHI1 profile across patient tissues and cancer cell-line models. AHI1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, AHI1 is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, AHI1 RNA expression shows 21,056 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight SKCM, KICH, and UVM as cancer lineages where AHI1 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 AHI1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AHI1 survival associations across molecular data types. AHI1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AHI1 RNA expression–survival associations across cancer types. High AHI1 expression shows unfavorable associations in LGG, LIHC and KIRC, but favorable associations in SKCM, PAAD and READ. The SKCM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify SKCM as the clearest survival context for AHI1 RNA expression.
This table summarizes AHI1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 4. The strongest signals are observed in KICH for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for AHI1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AHI1 shows lower tumor expression in KICH and KIRC and higher tumor expression in HNSC, COAD, CHOL and LIHC. The KICH box plot shows higher AHI1 RNA expression in normal versus tumor tissue (log2 FC = −1.564, t-test p < 0.001).
This table shows molecular features associated with AHI1 in patient tissues and cancer cell lines. In patient samples, AHI1 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, AHI1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
