protein kinase AMP-activated non-catalytic subunit beta 1Genealiases: AMPK · HAMPKb
Q-omics provides the consensus-scored PRKAB1 profile across patient tissues and cancer cell-line models. PRKAB1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PRKAB1 is differentially expressed in 8, with the highest sampling consensus in LIHC. Additionally, PRKAB1 protein abundance shows 25,365 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, LIHC, and PDAC as cancer lineages where PRKAB1 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 PRKAB1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PRKAB1 survival associations across molecular data types. PRKAB1 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (5) 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 PRKAB1 RNA expression–survival associations across cancer types. High PRKAB1 expression shows unfavorable associations in ACC, MESO and LIHC, but favorable associations in KIRC, BRCA and HNSC. 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 PRKAB1 RNA expression.
This table summarizes PRKAB1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 8. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PRKAB1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PRKAB1 shows lower tumor expression in LUSC, KIRC, KICH and LUAD and higher tumor expression in LIHC and CHOL. The LIHC box plot shows higher PRKAB1 RNA expression in tumor versus normal tissue (log2 FC = +0.911, t-test p < 0.001).
This table shows molecular features associated with PRKAB1 in patient tissues and cancer cell lines. In patient samples, PRKAB1 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, PRKAB1 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 LARGE_INTESTINE and BLOOD_Leukemia.
