Q-omics provides the consensus-scored AKAP8L profile across patient tissues and cancer cell-line models. AKAP8L expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, AKAP8L is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, AKAP8L protein abundance shows 25,784 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where AKAP8L 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 AKAP8L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AKAP8L survival associations across molecular data types. AKAP8L RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AKAP8L RNA expression–survival associations across cancer types. High AKAP8L expression shows unfavorable associations in KIRC, KICH, LIHC, COAD and ACC, but favorable associations in HNSC. The KIRC 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 KIRC as the clearest survival context for AKAP8L RNA expression.
This table summarizes AKAP8L 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 7. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for AKAP8L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AKAP8L shows higher tumor expression in HNSC, COAD, LIHC, STAD, LUSC and READ. The HNSC box plot shows higher AKAP8L RNA expression in tumor versus normal tissue (log2 FC = +0.777, t-test p < 0.001).
This table shows molecular features associated with AKAP8L in patient tissues and cancer cell lines. In patient samples, AKAP8L shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, AKAP8L 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 UPPER_AERODIGESTIVE_TRACT and BONE.
