Q-omics provides the consensus-scored DKK2 profile across patient tissues and cancer cell-line models. DKK2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, DKK2 is differentially expressed in 10, with the highest sampling consensus in BLCA. Additionally, DKK2 RNA expression shows 16,284 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRP, BLCA, and THYM as cancer lineages where DKK2 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 DKK2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DKK2 survival associations across molecular data types. DKK2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) 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 DKK2 RNA expression–survival associations across cancer types. High DKK2 expression shows unfavorable associations in KIRP, LUSC and UVM, but favorable associations in UCS, KIRC and SKCM. The KIRP 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 KIRP as the clearest survival context for DKK2 RNA expression.
This table summarizes DKK2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 2. The strongest signals are observed in BLCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for DKK2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DKK2 shows lower tumor expression in LUSC and LUAD and higher tumor expression in BLCA, STAD, LIHC and CHOL. The BLCA box plot shows higher DKK2 RNA expression in tumor versus normal tissue (log2 FC = +1.742, t-test p < 0.001).
This table shows molecular features associated with DKK2 in patient tissues and cancer cell lines. In patient samples, DKK2 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, DKK2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BONE.
