Q-omics provides the consensus-scored PIP4K2A profile across patient tissues and cancer cell-line models. PIP4K2A expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PIP4K2A is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, PIP4K2A protein abundance shows 29,410 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, HNSC, and LSCC as cancer lineages where PIP4K2A 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 PIP4K2A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PIP4K2A survival associations across molecular data types. PIP4K2A RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PIP4K2A RNA expression–survival associations across cancer types. High PIP4K2A expression shows unfavorable associations in KIRP, BLCA and STAD, but favorable associations in KIRC, CHOL and LUAD. 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 PIP4K2A RNA expression.
This table summarizes PIP4K2A 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 7. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PIP4K2A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PIP4K2A shows lower tumor expression in BLCA and LUSC and higher tumor expression in HNSC, KIRC, STAD and BRCA. The HNSC box plot shows higher PIP4K2A RNA expression in tumor versus normal tissue (log2 FC = +0.795, t-test p < 0.001).
This table shows molecular features associated with PIP4K2A in patient tissues and cancer cell lines. In patient samples, PIP4K2A 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, PIP4K2A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in CNS and BONE.
