Q-omics provides the consensus-scored PLEK2 profile across patient tissues and cancer cell-line models. PLEK2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, PLEK2 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, PLEK2 RNA expression shows 17,521 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight HNSC, and ACC as cancer lineages where PLEK2 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 PLEK2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PLEK2 survival associations across molecular data types. PLEK2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (5) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PLEK2 RNA expression–survival associations across cancer types. High PLEK2 expression shows unfavorable associations in HNSC, UVM, LUAD, ACC and LGG, but favorable associations in KIRC. The HNSC 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 HNSC as the clearest survival context for PLEK2 RNA expression.
This table summarizes PLEK2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PLEK2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PLEK2 shows lower tumor expression in THCA and higher tumor expression in HNSC, LUAD, KICH, COAD and LUSC. The HNSC box plot shows higher PLEK2 RNA expression in tumor versus normal tissue (log2 FC = +2.751, t-test p < 0.001).
This table shows molecular features associated with PLEK2 in patient tissues and cancer cell lines. In patient samples, PLEK2 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, PLEK2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Lymphoma.
