Q-omics provides the consensus-scored PIP4K2B profile across patient tissues and cancer cell-line models. PIP4K2B expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, PIP4K2B is differentially expressed in 12, with the highest sampling consensus in KIRP. Additionally, PIP4K2B protein abundance shows 26,179 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, KIRP, and GBM as cancer lineages where PIP4K2B 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 PIP4K2B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PIP4K2B survival associations across molecular data types. PIP4K2B RNA expression shows survival associations in the most cancer types (29), followed by mutation status (6) 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 PIP4K2B RNA expression–survival associations across cancer types. High PIP4K2B expression shows unfavorable associations in LIHC, ACC, MESO and HNSC, but favorable associations in BRCA and UCS. The LIHC 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 LIHC as the clearest survival context for PIP4K2B RNA expression.
This table summarizes PIP4K2B 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 4. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PIP4K2B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PIP4K2B shows lower tumor expression in KICH and higher tumor expression in KIRP, KIRC, HNSC, LIHC and CHOL. The KIRP box plot shows higher PIP4K2B RNA expression in tumor versus normal tissue (log2 FC = +0.686, t-test p < 0.001).
This table shows molecular features associated with PIP4K2B in patient tissues and cancer cell lines. In patient samples, PIP4K2B 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, PIP4K2B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
