Q-omics provides the consensus-scored AK9 profile across patient tissues and cancer cell-line models. AK9 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, AK9 is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, AK9 RNA expression shows 21,301 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight LUAD, KICH, and KIRP as cancer lineages where AK9 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 AK9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AK9 survival associations across molecular data types. AK9 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (8) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AK9 RNA expression–survival associations across cancer types. High AK9 expression shows unfavorable associations in LIHC, but favorable associations in LUAD, KIRC, PAAD, UCS and LAML. The LUAD Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .002). Together, the overview and detailed table identify LUAD as the clearest survival context for AK9 RNA expression.
This table summarizes AK9 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 2. The strongest signals are observed in KICH for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for AK9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AK9 shows lower tumor expression in KICH, LUSC, KIRC and THCA and higher tumor expression in LIHC and CHOL. The KICH box plot shows higher AK9 RNA expression in normal versus tumor tissue (log2 FC = −1.375, t-test p < 0.001).
This table shows molecular features associated with AK9 in patient tissues and cancer cell lines. In patient samples, AK9 shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, AK9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
