Q-omics provides the consensus-scored ACP5 profile across patient tissues and cancer cell-line models. ACP5 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ACP5 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, ACP5 RNA expression shows 15,723 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, HNSC, and LSCC as cancer lineages where ACP5 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 ACP5 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACP5 survival associations across molecular data types. ACP5 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3) 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 ACP5 RNA expression–survival associations across cancer types. High ACP5 expression shows unfavorable associations in ACC and LGG, but favorable associations in KIRC, CESC, KIRP and BRCA. 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 ACP5 RNA expression.
This table summarizes ACP5 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 4. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ACP5. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACP5 shows lower tumor expression in LUAD and LUSC and higher tumor expression in HNSC, THCA, STAD and KIRC. The HNSC box plot shows higher ACP5 RNA expression in tumor versus normal tissue (log2 FC = +2.025, t-test p < 0.001).
This table shows molecular features associated with ACP5 in patient tissues and cancer cell lines. In patient samples, ACP5 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ACP5 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
