Q-omics provides the consensus-scored BAAT profile across patient tissues and cancer cell-line models. BAAT expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, BAAT is differentially expressed in 12, with the highest sampling consensus in COAD. Additionally, BAAT RNA expression shows 12,588 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight ACC, COAD, and TGCT as cancer lineages where BAAT 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 BAAT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes BAAT survival associations across molecular data types. BAAT RNA expression shows survival associations in the most cancer types (23), followed by mutation status (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible BAAT RNA expression–survival associations across cancer types. High BAAT expression shows unfavorable associations in ACC, KICH, COAD and THCA, but favorable associations in LIHC and READ. 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 BAAT RNA expression.
This table summarizes BAAT 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 1. The strongest signals are observed in COAD for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for BAAT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. BAAT shows lower tumor expression in CHOL and LUSC and higher tumor expression in COAD, KIRC, STAD and READ. The COAD box plot shows higher BAAT RNA expression in tumor versus normal tissue (log2 FC = +0.436, t-test p < 0.001).
This table shows molecular features associated with BAAT in patient tissues and cancer cell lines. In patient samples, BAAT shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, BAAT 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 SOFT_TISSUE and BLOOD_Leukemia.
