Q-omics provides the consensus-scored AMPH profile across patient tissues and cancer cell-line models. AMPH expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, AMPH is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, AMPH protein abundance shows 26,471 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight BLCA, KIRC, and GBM as cancer lineages where AMPH 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 AMPH — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AMPH survival associations across molecular data types. AMPH RNA expression shows survival associations in the most cancer types (24), followed by mutation status (10) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AMPH RNA expression–survival associations across cancer types. High AMPH expression shows unfavorable associations in BLCA, KIRP, ACC, MESO and UVM, but favorable associations in BRCA. The BLCA Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify BLCA as the clearest survival context for AMPH RNA expression.
This table summarizes AMPH tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for AMPH. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AMPH shows lower tumor expression in KIRC, KICH, KIRP, UCEC and STAD and higher tumor expression in HNSC. The KIRC box plot shows higher AMPH RNA expression in normal versus tumor tissue (log2 FC = −1.980, t-test p < 0.001).
This table shows molecular features associated with AMPH in patient tissues and cancer cell lines. In patient samples, AMPH 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, AMPH 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 LUNG_NSCLC_LUSC and BLOOD_Leukemia.
