Q-omics provides the consensus-scored DYDC2 profile across patient tissues and cancer cell-line models. DYDC2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, DYDC2 is differentially expressed in 8, with the highest sampling consensus in KIRC. Additionally, DYDC2 RNA expression shows 13,130 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, and TGCT as cancer lineages where DYDC2 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 DYDC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DYDC2 survival associations across molecular data types. DYDC2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DYDC2 RNA expression–survival associations across cancer types. High DYDC2 expression shows unfavorable associations in KIRC, COAD, UVM and READ, but favorable associations in OV and MESO. The KIRC 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 KIRC as the clearest survival context for DYDC2 RNA expression.
This table summarizes DYDC2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 2. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for DYDC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DYDC2 shows lower tumor expression in KIRC, LUSC, LUAD and KICH and higher tumor expression in LIHC and BRCA. The KIRC box plot shows higher DYDC2 RNA expression in normal versus tumor tissue (log2 FC = −0.323, t-test p < 0.001).
This table shows molecular features associated with DYDC2 in patient tissues and cancer cell lines. In patient samples, DYDC2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, DYDC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BREAST and LUNG_SCLC.
