Q-omics provides the consensus-scored ANAPC16 profile across patient tissues and cancer cell-line models. ANAPC16 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, ANAPC16 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, ANAPC16 RNA expression shows 19,257 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight SKCM, KICH, and ACC as cancer lineages where ANAPC16 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 ANAPC16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ANAPC16 survival associations across molecular data types. ANAPC16 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) 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 ANAPC16 RNA expression–survival associations across cancer types. High ANAPC16 expression shows unfavorable associations in ACC, UVM and LAML, but favorable associations in SKCM, KIRC and THYM. The SKCM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify SKCM as the clearest survival context for ANAPC16 RNA expression.
This table summarizes ANAPC16 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ANAPC16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ANAPC16 shows lower tumor expression in KICH, THCA, BLCA, LUSC and LUAD and higher tumor expression in LIHC. The KICH box plot shows higher ANAPC16 RNA expression in normal versus tumor tissue (log2 FC = −1.617, t-test p < 0.001).
This table shows molecular features associated with ANAPC16 in patient tissues and cancer cell lines. In patient samples, ANAPC16 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, ANAPC16 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 SOFT_TISSUE and UPPER_AERODIGESTIVE_TRACT.
