Q-omics provides the consensus-scored MAPK1 profile across patient tissues and cancer cell-line models. MAPK1 expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, MAPK1 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, MAPK1 protein abundance shows 29,582 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where MAPK1 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 MAPK1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MAPK1 survival associations across molecular data types. MAPK1 RNA expression shows survival associations in the most cancer types (29), followed by mutation status (6) 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 MAPK1 RNA expression–survival associations across cancer types. High MAPK1 expression shows unfavorable associations in UVM, ACC, LIHC, PAAD and CESC, but favorable associations in KIRC. 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 MAPK1 RNA expression.
This table summarizes MAPK1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MAPK1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MAPK1 shows lower tumor expression in COAD and higher tumor expression in HNSC, KIRC, LIHC, CHOL and LUSC. The HNSC box plot shows higher MAPK1 RNA expression in tumor versus normal tissue (log2 FC = +0.624, t-test p < 0.001).
This table shows molecular features associated with MAPK1 in patient tissues and cancer cell lines. In patient samples, MAPK1 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, MAPK1 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.
