Q-omics provides the consensus-scored AKAP1 profile across patient tissues and cancer cell-line models. AKAP1 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, AKAP1 is differentially expressed in 14, with the highest sampling consensus in THCA. Additionally, AKAP1 protein abundance shows 21,861 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, THCA, and LSCC as cancer lineages where AKAP1 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 AKAP1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AKAP1 survival associations across molecular data types. AKAP1 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (6) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AKAP1 RNA expression–survival associations across cancer types. High AKAP1 expression shows unfavorable associations in CESC, HNSC and LIHC, but favorable associations in KIRC, LGG and READ. The KIRC 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 KIRC as the clearest survival context for AKAP1 RNA expression.
This table summarizes AKAP1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for AKAP1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AKAP1 shows lower tumor expression in THCA and BRCA and higher tumor expression in KIRP, PAAD, LIHC and LUAD. The THCA box plot shows higher AKAP1 RNA expression in normal versus tumor tissue (log2 FC = −0.761, t-test p < 0.001).
This table shows molecular features associated with AKAP1 in patient tissues and cancer cell lines. In patient samples, AKAP1 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, AKAP1 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 STOMACH and BLOOD_Lymphoma.
