mitogen-activated protein kinase kinase 4 pseudogene 1Genealiases: []
Q-omics provides the consensus-scored MAP2K4P1 profile across patient tissues and cancer cell-line models. MAP2K4P1 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, MAP2K4P1 is differentially expressed in 8, with the highest sampling consensus in KICH. Additionally, MAP2K4P1 RNA expression shows 17,821 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, KICH, and UVM as cancer lineages where MAP2K4P1 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 MAP2K4P1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MAP2K4P1 survival associations across molecular data types. MAP2K4P1 RNA expression shows survival associations in the most cancer types (19). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MAP2K4P1 RNA expression–survival associations across cancer types. High MAP2K4P1 expression shows unfavorable associations in LUSC and UVM, but favorable associations in KIRC, ESCA, BRCA and SKCM. 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 MAP2K4P1 RNA expression.
This table summarizes MAP2K4P1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for MAP2K4P1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MAP2K4P1 shows lower tumor expression in KICH, COAD, LUSC, THCA and READ and higher tumor expression in CHOL. The KICH box plot shows higher MAP2K4P1 RNA expression in normal versus tumor tissue (log2 FC = −0.292, t-test p < 0.001).
This table shows molecular features associated with MAP2K4P1 in patient tissues and cancer cell lines. In patient samples, MAP2K4P1 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, MAP2K4P1 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 BREAST.
