Q-omics provides the consensus-scored DIP2A profile across patient tissues and cancer cell-line models. DIP2A expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, DIP2A is differentially expressed in 8, with the highest sampling consensus in LIHC. Additionally, DIP2A RNA expression shows 21,005 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UVM, and LIHC as cancer lineages where DIP2A 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 DIP2A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DIP2A survival associations across molecular data types. DIP2A RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) 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 DIP2A RNA expression–survival associations across cancer types. High DIP2A expression shows unfavorable associations in UVM, ACC and COAD, but favorable associations in HNSC, BLCA and SKCM. The UVM Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify UVM as the clearest survival context for DIP2A RNA expression.
This table summarizes DIP2A 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 6. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for DIP2A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DIP2A shows lower tumor expression in THCA and higher tumor expression in LIHC, KIRC, CHOL, HNSC and PRAD. The LIHC box plot shows higher DIP2A RNA expression in tumor versus normal tissue (log2 FC = +0.946, t-test p < 0.001).
This table shows molecular features associated with DIP2A in patient tissues and cancer cell lines. In patient samples, DIP2A shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, DIP2A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
