Q-omics provides the consensus-scored AKAP17A profile across patient tissues and cancer cell-line models. AKAP17A expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, AKAP17A is differentially expressed in 11, with the highest sampling consensus in COAD. Additionally, AKAP17A RNA expression shows 19,306 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRP, COAD, and THYM as cancer lineages where AKAP17A 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 AKAP17A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AKAP17A survival associations across molecular data types. AKAP17A RNA expression shows survival associations in the most cancer types (27), followed by mutation status (5) 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 AKAP17A RNA expression–survival associations across cancer types. High AKAP17A expression shows unfavorable associations in KIRP and KIRC, but favorable associations in BLCA, MESO, SKCM and PAAD. The KIRP 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 KIRP as the clearest survival context for AKAP17A RNA expression.
This table summarizes AKAP17A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in COAD for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for AKAP17A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AKAP17A shows lower tumor expression in THCA and KICH and higher tumor expression in COAD, LIHC, KIRC and STAD. The COAD box plot shows higher AKAP17A RNA expression in tumor versus normal tissue (log2 FC = +0.949, t-test p < 0.001).
This table shows molecular features associated with AKAP17A in patient tissues and cancer cell lines. In patient samples, AKAP17A shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, AKAP17A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BLOOD_Leukemia.
