Q-omics provides the consensus-scored TAF9B profile across patient tissues and cancer cell-line models. TAF9B expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in OV. Among the 18 cancer types available for tumor–normal comparison, TAF9B is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, TAF9B RNA expression shows 20,632 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight OV, KICH, and KIRP as cancer lineages where TAF9B 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 TAF9B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TAF9B survival associations across molecular data types. TAF9B RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) 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 TAF9B RNA expression–survival associations across cancer types. High TAF9B expression shows unfavorable associations in STAD, LUSC and KICH, but favorable associations in OV, KIRC and BRCA. The OV 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 OV as the clearest survival context for TAF9B RNA expression.
This table summarizes TAF9B tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 2. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for TAF9B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TAF9B shows lower tumor expression in KICH, THCA and UCEC and higher tumor expression in LIHC, HNSC and BRCA. The KICH box plot shows higher TAF9B RNA expression in normal versus tumor tissue (log2 FC = −1.693, t-test p < 0.001).
This table shows molecular features associated with TAF9B in patient tissues and cancer cell lines. In patient samples, TAF9B shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, TAF9B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and BLOOD_Leukemia.
