Q-omics provides the consensus-scored ACP4 profile across patient tissues and cancer cell-line models. ACP4 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ACP4 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, ACP4 RNA expression shows 15,902 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight MESO, HNSC, and GBM as cancer lineages where ACP4 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 ACP4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACP4 survival associations across molecular data types. ACP4 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ACP4 RNA expression–survival associations across cancer types. High ACP4 expression shows unfavorable associations in MESO, KIRC, KICH, READ, LIHC and SCLC. The MESO 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 MESO as the clearest survival context for ACP4 RNA expression.
This table summarizes ACP4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for ACP4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACP4 shows lower tumor expression in THCA and higher tumor expression in HNSC, COAD, STAD, LIHC and KIRC. The HNSC box plot shows higher ACP4 RNA expression in tumor versus normal tissue (log2 FC = +0.252, t-test p = .001).
This table shows molecular features associated with ACP4 in patient tissues and cancer cell lines. In patient samples, ACP4 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, ACP4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BONE.
