Q-omics provides the consensus-scored RAD9A profile across patient tissues and cancer cell-line models. RAD9A expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, RAD9A is differentially expressed in 16, with the highest sampling consensus in KIRC. Additionally, RAD9A RNA expression shows 18,924 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KIRC as cancer lineages where RAD9A 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 RAD9A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RAD9A survival associations across molecular data types. RAD9A RNA expression shows survival associations in the most cancer types (22), followed by mutation status (1) 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 RAD9A RNA expression–survival associations across cancer types. High RAD9A expression shows unfavorable associations in ACC, KIRC, KICH and LGG, but favorable associations in HNSC and BLCA. 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 RAD9A RNA expression.
This table summarizes RAD9A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for RAD9A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RAD9A shows higher tumor expression in KIRC, LIHC, KIRP, STAD, COAD and LUSC. The KIRC box plot shows higher RAD9A RNA expression in tumor versus normal tissue (log2 FC = +0.641, t-test p < 0.001).
This table shows molecular features associated with RAD9A in patient tissues and cancer cell lines. In patient samples, RAD9A shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, RAD9A 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 BONE and BLOOD_Leukemia.
