Q-omics provides the consensus-scored PDK2 profile across patient tissues and cancer cell-line models. PDK2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PDK2 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, PDK2 protein abundance shows 29,462 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, KICH, and GBM as cancer lineages where PDK2 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 PDK2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PDK2 survival associations across molecular data types. PDK2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) 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 PDK2 RNA expression–survival associations across cancer types. High PDK2 expression shows unfavorable associations in SCLC, but favorable associations in KIRC, LUAD, UVM, ACC and MESO. 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 PDK2 RNA expression.
This table summarizes PDK2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 9. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PDK2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PDK2 shows lower tumor expression in KICH, COAD, UCEC, BLCA, KIRP and BRCA. The KICH box plot shows higher PDK2 RNA expression in normal versus tumor tissue (log2 FC = −1.535, t-test p < 0.001).
This table shows molecular features associated with PDK2 in patient tissues and cancer cell lines. In patient samples, PDK2 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, PDK2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and UPPER_AERODIGESTIVE_TRACT.
