Q-omics provides the consensus-scored AKAP9 profile across patient tissues and cancer cell-line models. AKAP9 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, AKAP9 is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, AKAP9 protein abundance shows 24,271 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KIRC, COAD, and HNSC as cancer lineages where AKAP9 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 AKAP9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AKAP9 survival associations across molecular data types. AKAP9 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AKAP9 RNA expression–survival associations across cancer types. High AKAP9 expression shows unfavorable associations in CESC and UVM, but favorable associations in KIRC, SKCM, UCS and LAML. 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 AKAP9 RNA expression.
This table summarizes AKAP9 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 10. The strongest signals are observed in COAD for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for AKAP9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AKAP9 shows lower tumor expression in COAD, KIRC, THCA and KIRP and higher tumor expression in CHOL and BRCA. The COAD box plot shows higher AKAP9 RNA expression in normal versus tumor tissue (log2 FC = −1.516, t-test p < 0.001).
This table shows molecular features associated with AKAP9 in patient tissues and cancer cell lines. In patient samples, AKAP9 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, AKAP9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
