Q-omics provides the consensus-scored CHGA profile across patient tissues and cancer cell-line models. CHGA expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, CHGA is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, CHGA protein abundance shows 22,542 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, COAD, and GBM as cancer lineages where CHGA 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 CHGA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CHGA survival associations across molecular data types. CHGA RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CHGA RNA expression–survival associations across cancer types. High CHGA expression shows unfavorable associations in KIRC, KIRP, BLCA and LIHC, but favorable associations in BRCA and PAAD. 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 CHGA RNA expression.
This table summarizes CHGA 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 KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for CHGA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CHGA shows lower tumor expression in COAD, KIRC, KICH, READ and STAD and higher tumor expression in LIHC. The COAD box plot shows higher CHGA RNA expression in normal versus tumor tissue (log2 FC = −5.803, t-test p < 0.001).
This table shows molecular features associated with CHGA in patient tissues and cancer cell lines. In patient samples, CHGA shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, CHGA 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 CNS and BLOOD_Lymphoma.
