Q-omics provides the consensus-scored CGAS profile across patient tissues and cancer cell-line models. CGAS expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, CGAS is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, CGAS protein abundance shows 21,888 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight UVM, HNSC, and LUAD as cancer lineages where CGAS 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 CGAS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CGAS survival associations across molecular data types. CGAS RNA expression shows survival associations in the most cancer types (21), followed by mutation status (2) 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 CGAS RNA expression–survival associations across cancer types. High CGAS expression shows unfavorable associations in UVM, MESO, LGG, LIHC and COAD, but favorable associations in SKCM. The UVM 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 UVM as the clearest survival context for CGAS RNA expression.
This table summarizes CGAS 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 6. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CGAS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CGAS shows lower tumor expression in KICH and higher tumor expression in HNSC, KIRC, STAD, LUAD and BLCA. The HNSC box plot shows higher CGAS RNA expression in tumor versus normal tissue (log2 FC = +2.150, t-test p < 0.001).
This table shows molecular features associated with CGAS in patient tissues and cancer cell lines. In patient samples, CGAS shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, CGAS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Lymphoma.
