Q-omics provides the consensus-scored CCDC86 profile across patient tissues and cancer cell-line models. CCDC86 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, CCDC86 is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, CCDC86 protein abundance shows 26,473 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, KIRC, and LSCC as cancer lineages where CCDC86 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 CCDC86 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CCDC86 survival associations across molecular data types. CCDC86 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CCDC86 RNA expression–survival associations across cancer types. High CCDC86 expression shows unfavorable associations in HNSC, LIHC, KICH, BLCA, MESO and LGG. 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 CCDC86 RNA expression.
This table summarizes CCDC86 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 8. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CCDC86. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CCDC86 shows higher tumor expression in KIRC, COAD, KIRP, HNSC, STAD and LIHC. The KIRC box plot shows higher CCDC86 RNA expression in tumor versus normal tissue (log2 FC = +0.785, t-test p < 0.001).
This table shows molecular features associated with CCDC86 in patient tissues and cancer cell lines. In patient samples, CCDC86 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, CCDC86 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Lymphoma.
