Q-omics provides the consensus-scored AMBP profile across patient tissues and cancer cell-line models. AMBP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, AMBP is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, AMBP protein abundance shows 28,044 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, KICH, and PDAC as cancer lineages where AMBP 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 AMBP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AMBP survival associations across molecular data types. AMBP RNA expression shows survival associations in the most cancer types (24), followed by mutation status (6) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AMBP RNA expression–survival associations across cancer types. High AMBP expression shows unfavorable associations in ACC and LGG, but favorable associations in KIRC, COAD, UCEC and KICH. The ACC 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 ACC as the clearest survival context for AMBP RNA expression.
This table summarizes AMBP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 9. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for AMBP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AMBP shows lower tumor expression in KICH, KIRC, THCA, LUSC and LIHC and higher tumor expression in COAD. The KICH box plot shows higher AMBP RNA expression in normal versus tumor tissue (log2 FC = −3.961, t-test p < 0.001).
This table shows molecular features associated with AMBP in patient tissues and cancer cell lines. In patient samples, AMBP shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, AMBP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BONE and BREAST.
