Q-omics provides the consensus-scored MAPK8 profile across patient tissues and cancer cell-line models. MAPK8 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, MAPK8 is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, MAPK8 RNA expression shows 21,780 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KICH as cancer lineages where MAPK8 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 MAPK8 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MAPK8 survival associations across molecular data types. MAPK8 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MAPK8 RNA expression–survival associations across cancer types. High MAPK8 expression shows unfavorable associations in ACC and CESC, but favorable associations in KIRC, LGG, THYM and UCS. The ACC 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 ACC as the clearest survival context for MAPK8 RNA expression.
This table summarizes MAPK8 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 2. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for MAPK8. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MAPK8 shows lower tumor expression in KICH, THCA and KIRC and higher tumor expression in HNSC, STAD and LIHC. The KICH box plot shows higher MAPK8 RNA expression in normal versus tumor tissue (log2 FC = −1.069, t-test p < 0.001).
This table shows molecular features associated with MAPK8 in patient tissues and cancer cell lines. In patient samples, MAPK8 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, MAPK8 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and LUNG_NSCLC_LUAD.
