Q-omics provides the consensus-scored MACROH2A2 profile across patient tissues and cancer cell-line models. MACROH2A2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, MACROH2A2 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, MACROH2A2 protein abundance shows 21,472 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight LGG, KICH, and HNSC as cancer lineages where MACROH2A2 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 MACROH2A2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MACROH2A2 survival associations across molecular data types. MACROH2A2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MACROH2A2 RNA expression–survival associations across cancer types. High MACROH2A2 expression shows unfavorable associations in ACC, READ and LAML, but favorable associations in LGG, UCS and OV. The LGG 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 LGG as the clearest survival context for MACROH2A2 RNA expression.
This table summarizes MACROH2A2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MACROH2A2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MACROH2A2 shows lower tumor expression in KICH, KIRC and STAD and higher tumor expression in BLCA, LUSC and UCEC. The KICH box plot shows higher MACROH2A2 RNA expression in normal versus tumor tissue (log2 FC = −2.550, t-test p < 0.001).
This table shows molecular features associated with MACROH2A2 in patient tissues and cancer cell lines. In patient samples, MACROH2A2 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, MACROH2A2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LIVER and BONE.
