Q-omics provides the consensus-scored PDK3 profile across patient tissues and cancer cell-line models. PDK3 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, PDK3 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, PDK3 protein abundance shows 27,010 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KICH, and HNSC as cancer lineages where PDK3 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 PDK3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PDK3 survival associations across molecular data types. PDK3 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PDK3 RNA expression–survival associations across cancer types. High PDK3 expression shows unfavorable associations in KICH, UCS, LIHC and LGG, but favorable associations in KIRC and PAAD. The KICH 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 KICH as the clearest survival context for PDK3 RNA expression.
This table summarizes PDK3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PDK3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PDK3 shows higher tumor expression in HNSC, BLCA, COAD, UCEC, LUSC and BRCA. The HNSC box plot shows higher PDK3 RNA expression in tumor versus normal tissue (log2 FC = +0.613, t-test p < 0.001).
This table shows molecular features associated with PDK3 in patient tissues and cancer cell lines. In patient samples, PDK3 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, PDK3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUSC, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
