interaction protein for cytohesin exchange factors 1Genealiases: PIP3-E · PIP3E
Q-omics provides the consensus-scored IPCEF1 profile across patient tissues and cancer cell-line models. IPCEF1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, IPCEF1 is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, IPCEF1 RNA expression shows 20,889 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SKCM, KIRC, and GBM as cancer lineages where IPCEF1 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 IPCEF1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IPCEF1 survival associations across molecular data types. IPCEF1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IPCEF1 RNA expression–survival associations across cancer types. High IPCEF1 expression shows unfavorable associations in UVM, but favorable associations in SKCM, HNSC, KIRC, BLCA and LIHC. The SKCM 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 SKCM as the clearest survival context for IPCEF1 RNA expression.
This table summarizes IPCEF1 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 2. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for IPCEF1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IPCEF1 shows lower tumor expression in KIRC, THCA, KICH, KIRP, COAD and HNSC. The KIRC box plot shows higher IPCEF1 RNA expression in normal versus tumor tissue (log2 FC = −0.942, t-test p < 0.001).
This table shows molecular features associated with IPCEF1 in patient tissues and cancer cell lines. In patient samples, IPCEF1 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, IPCEF1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
