Q-omics provides the consensus-scored APBA1 profile across patient tissues and cancer cell-line models. APBA1 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, APBA1 is differentially expressed in 13, with the highest sampling consensus in THCA. Additionally, APBA1 protein abundance shows 24,926 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, THCA, and GBM as cancer lineages where APBA1 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 APBA1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APBA1 survival associations across molecular data types. APBA1 RNA expression shows survival associations in the most cancer types (20), 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 APBA1 RNA expression–survival associations across cancer types. High APBA1 expression shows unfavorable associations in UVM, BLCA, STAD and HNSC, but favorable associations in KIRC and PAAD. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for APBA1 RNA expression.
This table summarizes APBA1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for APBA1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APBA1 shows lower tumor expression in THCA, COAD, KICH and BLCA and higher tumor expression in LUAD and HNSC. The THCA box plot shows higher APBA1 RNA expression in normal versus tumor tissue (log2 FC = −1.002, t-test p < 0.001).
This table shows molecular features associated with APBA1 in patient tissues and cancer cell lines. In patient samples, APBA1 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, APBA1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in BONE and LARGE_INTESTINE.
