Q-omics provides the consensus-scored CD3D profile across patient tissues and cancer cell-line models. CD3D expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, CD3D is differentially expressed in 9, with the highest sampling consensus in KIRC. Additionally, CD3D RNA expression shows 19,092 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where CD3D 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 CD3D — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CD3D survival associations across molecular data types. CD3D RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CD3D RNA expression–survival associations across cancer types. High CD3D expression shows unfavorable associations in UVM, but favorable associations in SKCM, HNSC, BLCA, UCEC and CESC. The SKCM 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 SKCM as the clearest survival context for CD3D RNA expression.
This table summarizes CD3D tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 2. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CD3D. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CD3D shows lower tumor expression in COAD and LUSC and higher tumor expression in KIRC, STAD, LIHC and KIRP. The KIRC box plot shows higher CD3D RNA expression in tumor versus normal tissue (log2 FC = +2.863, t-test p < 0.001).
This table shows molecular features associated with CD3D in patient tissues and cancer cell lines. In patient samples, CD3D shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, CD3D RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
