Q-omics provides the consensus-scored TP53 profile across patient tissues and cancer cell-line models. TP53 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, TP53 is differentially expressed in 13, with the highest sampling consensus in KIRP. Additionally, TP53 mutation status shows 26,142 significant gene co-expression associations, with the highest sampling consensus in BRCA. Together, these results highlight ACC, KIRP, and BRCA as cancer lineages where TP53 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 TP53 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TP53 survival associations across molecular data types. TP53 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (17) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TP53 RNA expression–survival associations across cancer types. High TP53 expression shows unfavorable associations in ACC, UCS, MESO and LGG, but favorable associations in STAD and CESC. 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 TP53 RNA expression.
This table summarizes TP53 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 KIRP for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for TP53. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TP53 shows lower tumor expression in KICH and higher tumor expression in KIRP, KIRC, STAD, LUAD and LIHC. The KIRP box plot shows higher TP53 RNA expression in tumor versus normal tissue (log2 FC = +1.476, t-test p < 0.001).
This table shows molecular features associated with TP53 in patient tissues and cancer cell lines. In patient samples, TP53 shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, TP53 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
