Q-omics provides the consensus-scored AKAP10 profile across patient tissues and cancer cell-line models. AKAP10 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, AKAP10 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, AKAP10 RNA expression shows 20,651 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UCS, HNSC, and ACC as cancer lineages where AKAP10 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 AKAP10 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AKAP10 survival associations across molecular data types. AKAP10 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (7) 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 AKAP10 RNA expression–survival associations across cancer types. High AKAP10 expression shows unfavorable associations in KICH and UVM, but favorable associations in UCS, ESCA, KIRC and BRCA. The UCS Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify UCS as the clearest survival context for AKAP10 RNA expression.
This table summarizes AKAP10 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AKAP10. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AKAP10 shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, KIRC, LIHC and CHOL. The HNSC box plot shows higher AKAP10 RNA expression in tumor versus normal tissue (log2 FC = +0.656, t-test p < 0.001).
This table shows molecular features associated with AKAP10 in patient tissues and cancer cell lines. In patient samples, AKAP10 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, AKAP10 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BREAST and UPPER_AERODIGESTIVE_TRACT.
