Q-omics provides the consensus-scored ZCCHC2 profile across patient tissues and cancer cell-line models. ZCCHC2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ZCCHC2 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, ZCCHC2 RNA expression shows 20,552 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KIRC as cancer lineages where ZCCHC2 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 ZCCHC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZCCHC2 survival associations across molecular data types. ZCCHC2 RNA expression shows survival associations in the most cancer types (26), 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 ZCCHC2 RNA expression–survival associations across cancer types. High ZCCHC2 expression shows unfavorable associations in ACC, UCEC and STAD, but favorable associations in KIRC, SKCM and LUSC. The ACC 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 ACC as the clearest survival context for ZCCHC2 RNA expression.
This table summarizes ZCCHC2 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 1. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ZCCHC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZCCHC2 shows lower tumor expression in THCA, KICH and COAD and higher tumor expression in KIRC, HNSC and STAD. The KIRC box plot shows higher ZCCHC2 RNA expression in tumor versus normal tissue (log2 FC = +0.719, t-test p < 0.001).
This table shows molecular features associated with ZCCHC2 in patient tissues and cancer cell lines. In patient samples, ZCCHC2 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, ZCCHC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and LARGE_INTESTINE.
