Q-omics provides the consensus-scored CHGB profile across patient tissues and cancer cell-line models. CHGB expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, CHGB is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, CHGB protein abundance shows 22,065 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight HNSC, KIRC, and GBM as cancer lineages where CHGB 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 CHGB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CHGB survival associations across molecular data types. CHGB RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CHGB RNA expression–survival associations across cancer types. High CHGB expression shows unfavorable associations in HNSC and BLCA, but favorable associations in UVM, ACC, LGG and PAAD. The HNSC 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 HNSC as the clearest survival context for CHGB RNA expression.
This table summarizes CHGB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CHGB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CHGB shows lower tumor expression in KIRC, COAD, KICH, UCEC and READ and higher tumor expression in LUAD. The KIRC box plot shows higher CHGB RNA expression in normal versus tumor tissue (log2 FC = −3.383, t-test p < 0.001).
This table shows molecular features associated with CHGB in patient tissues and cancer cell lines. In patient samples, CHGB 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, CHGB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Lymphoma.
