Q-omics provides the consensus-scored DNAJC16 profile across patient tissues and cancer cell-line models. DNAJC16 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, DNAJC16 is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, DNAJC16 RNA expression shows 21,388 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KICH as cancer lineages where DNAJC16 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 DNAJC16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DNAJC16 survival associations across molecular data types. DNAJC16 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) 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 DNAJC16 RNA expression–survival associations across cancer types. High DNAJC16 expression shows unfavorable associations in ACC, LGG and DLBC, but favorable associations in KIRC, READ and COAD. 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 DNAJC16 RNA expression.
This table summarizes DNAJC16 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 4. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for DNAJC16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DNAJC16 shows lower tumor expression in KICH, KIRP, THCA and KIRC and higher tumor expression in BLCA and STAD. The KICH box plot shows higher DNAJC16 RNA expression in normal versus tumor tissue (log2 FC = −1.464, t-test p < 0.001).
This table shows molecular features associated with DNAJC16 in patient tissues and cancer cell lines. In patient samples, DNAJC16 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, DNAJC16 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 BONE and BLOOD_Leukemia.
