Q-omics provides the consensus-scored CGA profile across patient tissues and cancer cell-line models. CGA expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, CGA is differentially expressed in 7, with the highest sampling consensus in KIRC. Additionally, CGA RNA expression shows 9,050 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight MESO, KIRC, and TGCT as cancer lineages where CGA 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 CGA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CGA survival associations across molecular data types. CGA RNA expression shows survival associations in the most cancer types (22), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CGA RNA expression–survival associations across cancer types. High CGA expression shows unfavorable associations in MESO, UCEC, KIRC, HNSC and DLBC, but favorable associations in BRCA. The MESO Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify MESO as the clearest survival context for CGA RNA expression.
This table summarizes CGA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CGA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CGA shows lower tumor expression in KIRC and KIRP and higher tumor expression in BRCA, THCA, HNSC and LUSC. The KIRC box plot shows higher CGA RNA expression in normal versus tumor tissue (log2 FC = −0.813, t-test p < 0.001).
This table shows molecular features associated with CGA in patient tissues and cancer cell lines. In patient samples, CGA 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, CGA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BREAST.
