Q-omics provides the consensus-scored DAPP1 profile across patient tissues and cancer cell-line models. DAPP1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, DAPP1 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, DAPP1 protein abundance shows 18,774 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight SKCM, KIRC, and HNSC as cancer lineages where DAPP1 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 DAPP1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DAPP1 survival associations across molecular data types. DAPP1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DAPP1 RNA expression–survival associations across cancer types. High DAPP1 expression shows unfavorable associations in UVM, KIRP, LGG, PAAD and THYM, but favorable associations in SKCM. 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 DAPP1 RNA expression.
This table summarizes DAPP1 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 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DAPP1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DAPP1 shows lower tumor expression in KICH and UCEC and higher tumor expression in KIRC, LUAD, BRCA and THCA. The KIRC box plot shows higher DAPP1 RNA expression in tumor versus normal tissue (log2 FC = +0.892, t-test p < 0.001).
This table shows molecular features associated with DAPP1 in patient tissues and cancer cell lines. In patient samples, DAPP1 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, DAPP1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
