Q-omics provides the consensus-scored CYP11A1 profile across patient tissues and cancer cell-line models. CYP11A1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, CYP11A1 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, CYP11A1 protein abundance shows 23,155 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, HNSC, and LSCC as cancer lineages where CYP11A1 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 CYP11A1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CYP11A1 survival associations across molecular data types. CYP11A1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (9) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CYP11A1 RNA expression–survival associations across cancer types. High CYP11A1 expression shows unfavorable associations in KIRC, GBM, SKCM and LGG, but favorable associations in MESO and UCEC. The KIRC 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 KIRC as the clearest survival context for CYP11A1 RNA expression.
This table summarizes CYP11A1 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 HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for CYP11A1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CYP11A1 shows lower tumor expression in HNSC, COAD, LUAD, BRCA and KIRC and higher tumor expression in KICH. The HNSC box plot shows higher CYP11A1 RNA expression in normal versus tumor tissue (log2 FC = −1.634, t-test p < 0.001).
This table shows molecular features associated with CYP11A1 in patient tissues and cancer cell lines. In patient samples, CYP11A1 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, CYP11A1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BONE.
