Q-omics provides the consensus-scored ZC2HC1B profile across patient tissues and cancer cell-line models. ZC2HC1B expression is associated with patient survival in 15 of 34 cancer types, with the highest sampling consensus in THYM. Additionally, ZC2HC1B protein abundance shows 21,227 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight THYM, and GBM as cancer lineages where ZC2HC1B 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 ZC2HC1B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZC2HC1B survival associations across molecular data types. ZC2HC1B RNA expression shows survival associations in the most cancer types (15), followed by mutation status (1) 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 ZC2HC1B RNA expression–survival associations across cancer types. High ZC2HC1B expression shows unfavorable associations in THYM, LUAD, UVM, LIHC, LUSC and BLCA. The THYM 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 THYM as the clearest survival context for ZC2HC1B RNA expression.
This table shows molecular features associated with ZC2HC1B in patient tissues and cancer cell lines. In patient samples, ZC2HC1B 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, ZC2HC1B 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 LARGE_INTESTINE.
