Q-omics provides the consensus-scored ZCCHC17 profile across patient tissues and cancer cell-line models. ZCCHC17 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, ZCCHC17 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, ZCCHC17 protein abundance shows 24,515 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KICH, and HNSC as cancer lineages where ZCCHC17 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 ZCCHC17 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZCCHC17 survival associations across molecular data types. ZCCHC17 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ZCCHC17 RNA expression–survival associations across cancer types. High ZCCHC17 expression shows unfavorable associations in KICH, LIHC, ACC, LAML and LGG, but favorable associations in KIRC. The KICH Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .004). Together, the overview and detailed table identify KICH as the clearest survival context for ZCCHC17 RNA expression.
This table summarizes ZCCHC17 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 8. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ZCCHC17. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZCCHC17 shows lower tumor expression in KICH and THCA and higher tumor expression in HNSC, LIHC, STAD and BRCA. The HNSC box plot shows higher ZCCHC17 RNA expression in tumor versus normal tissue (log2 FC = +0.774, t-test p < 0.001).
This table shows molecular features associated with ZCCHC17 in patient tissues and cancer cell lines. In patient samples, ZCCHC17 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, ZCCHC17 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and CNS.
