Q-omics provides the consensus-scored DPY30 profile across patient tissues and cancer cell-line models. DPY30 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, DPY30 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, DPY30 protein abundance shows 31,256 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, HNSC, and GBM as cancer lineages where DPY30 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 DPY30 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DPY30 survival associations across molecular data types. DPY30 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (1) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DPY30 RNA expression–survival associations across cancer types. High DPY30 expression shows unfavorable associations in LIHC, UVM, HNSC, ACC, KIRP and KICH. The LIHC 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 LIHC as the clearest survival context for DPY30 RNA expression.
This table summarizes DPY30 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for DPY30. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DPY30 shows higher tumor expression in HNSC, KIRC, BLCA, LIHC, LUAD and COAD. The HNSC box plot shows higher DPY30 RNA expression in tumor versus normal tissue (log2 FC = +0.987, t-test p < 0.001).
This table shows molecular features associated with DPY30 in patient tissues and cancer cell lines. In patient samples, DPY30 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, DPY30 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and UPPER_AERODIGESTIVE_TRACT.
