Q-omics provides the consensus-scored ACP6 profile across patient tissues and cancer cell-line models. ACP6 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, ACP6 is differentially expressed in 16, with the highest sampling consensus in COAD. Additionally, ACP6 RNA expression shows 18,965 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KICH, COAD, and ACC as cancer lineages where ACP6 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 ACP6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACP6 survival associations across molecular data types. ACP6 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ACP6 RNA expression–survival associations across cancer types. High ACP6 expression shows unfavorable associations in KICH, ACC and LGG, but favorable associations in UCEC, BLCA and SKCM. The KICH Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .003). Together, the overview and detailed table identify KICH as the clearest survival context for ACP6 RNA expression.
This table summarizes ACP6 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 6. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ACP6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACP6 shows lower tumor expression in KICH and higher tumor expression in COAD, BLCA, LIHC, LUAD and PAAD. The COAD box plot shows higher ACP6 RNA expression in tumor versus normal tissue (log2 FC = +0.837, t-test p < 0.001).
This table shows molecular features associated with ACP6 in patient tissues and cancer cell lines. In patient samples, ACP6 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, ACP6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and BLOOD_Leukemia.
