Q-omics provides the consensus-scored CHAT profile across patient tissues and cancer cell-line models. CHAT 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, CHAT is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, CHAT RNA expression shows 11,248 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, COAD, and TGCT as cancer lineages where CHAT 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 CHAT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CHAT survival associations across molecular data types. CHAT RNA expression shows survival associations in the most cancer types (23), followed by mutation status (8) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CHAT RNA expression–survival associations across cancer types. High CHAT expression shows unfavorable associations in KIRC, LIHC, BLCA, MESO, COAD 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 CHAT RNA expression.
This table summarizes CHAT tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 4. The strongest signals are observed in COAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CHAT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CHAT shows lower tumor expression in COAD, STAD and READ and higher tumor expression in HNSC, KIRC and BRCA. The COAD box plot shows higher CHAT RNA expression in normal versus tumor tissue (log2 FC = −0.624, t-test p < 0.001).
This table shows molecular features associated with CHAT in patient tissues and cancer cell lines. In patient samples, CHAT 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, CHAT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in OESOPHAGUS and BONE.
