Q-omics provides the consensus-scored DIP2C profile across patient tissues and cancer cell-line models. DIP2C expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, DIP2C is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, DIP2C protein abundance shows 24,381 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, THCA, and GBM as cancer lineages where DIP2C 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 DIP2C — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DIP2C survival associations across molecular data types. DIP2C RNA expression shows survival associations in the most cancer types (24), followed by mutation status (9) 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 DIP2C RNA expression–survival associations across cancer types. High DIP2C expression shows unfavorable associations in BLCA, CESC, UVM and ACC, but favorable associations in KIRC and LGG. The KIRC 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 KIRC as the clearest survival context for DIP2C RNA expression.
This table summarizes DIP2C 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 7. The strongest signals are observed in THCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DIP2C. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DIP2C shows lower tumor expression in THCA, BLCA, KICH, BRCA and READ and higher tumor expression in KIRC. The THCA box plot shows higher DIP2C RNA expression in normal versus tumor tissue (log2 FC = −1.397, t-test p < 0.001).
This table shows molecular features associated with DIP2C in patient tissues and cancer cell lines. In patient samples, DIP2C 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, DIP2C RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
