Q-omics provides the consensus-scored AAMP profile across patient tissues and cancer cell-line models. AAMP expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, AAMP is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, AAMP RNA expression shows 19,248 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, COAD, and ACC as cancer lineages where AAMP 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 AAMP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AAMP survival associations across molecular data types. AAMP RNA expression shows survival associations in the most cancer types (25), followed by mutation status (2) 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 AAMP RNA expression–survival associations across cancer types. High AAMP expression shows unfavorable associations in LIHC, ACC, KICH and KIRP, but favorable associations in KIRC and SCLC. The LIHC 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 LIHC as the clearest survival context for AAMP RNA expression.
This table summarizes AAMP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, 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 AAMP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AAMP shows lower tumor expression in KICH and THCA and higher tumor expression in COAD, LIHC, HNSC and LUSC. The COAD box plot shows higher AAMP RNA expression in tumor versus normal tissue (log2 FC = +0.315, t-test p < 0.001).
This table shows molecular features associated with AAMP in patient tissues and cancer cell lines. In patient samples, AAMP 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, AAMP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
