Q-omics provides the consensus-scored DAGLA profile across patient tissues and cancer cell-line models. DAGLA expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, DAGLA is differentially expressed in 12, with the highest sampling consensus in LIHC. Additionally, DAGLA protein abundance shows 21,788 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UCS, LIHC, and GBM as cancer lineages where DAGLA 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 DAGLA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DAGLA survival associations across molecular data types. DAGLA RNA expression shows survival associations in the most cancer types (21), followed by mutation status (7) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DAGLA RNA expression–survival associations across cancer types. High DAGLA expression shows unfavorable associations in LIHC, UCEC and BLCA, but favorable associations in UCS, HNSC and READ. The UCS Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .004). Together, the overview and detailed table identify UCS as the clearest survival context for DAGLA RNA expression.
This table summarizes DAGLA 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 9. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DAGLA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DAGLA shows lower tumor expression in HNSC, KICH and LUSC and higher tumor expression in LIHC, BLCA and STAD. The LIHC box plot shows higher DAGLA RNA expression in tumor versus normal tissue (log2 FC = +1.407, t-test p < 0.001).
This table shows molecular features associated with DAGLA in patient tissues and cancer cell lines. In patient samples, DAGLA shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, DAGLA 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 LUNG_SCLC and BONE.
