DNA damage inducible transcript 3Genealiases: AltDDIT3 · C/EBPzeta · CEBPZ · CHOP · CHOP-10 · CHOP10
Q-omics provides the consensus-scored DDIT3 profile across patient tissues and cancer cell-line models. DDIT3 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, DDIT3 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, DDIT3 RNA expression shows 16,950 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, HNSC, and UVM as cancer lineages where DDIT3 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 DDIT3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DDIT3 survival associations across molecular data types. DDIT3 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DDIT3 RNA expression–survival associations across cancer types. High DDIT3 expression shows unfavorable associations in KIRC, ESCA, MESO, UVM, KICH and LIHC. The KIRC 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 KIRC as the clearest survival context for DDIT3 RNA expression.
This table summarizes DDIT3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for DDIT3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DDIT3 shows higher tumor expression in HNSC, KIRP, COAD, LIHC, KICH and LUSC. The HNSC box plot shows higher DDIT3 RNA expression in tumor versus normal tissue (log2 FC = +1.675, t-test p < 0.001).
This table shows molecular features associated with DDIT3 in patient tissues and cancer cell lines. In patient samples, DDIT3 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, DDIT3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and BLOOD_Lymphoma.
