Q-omics provides the consensus-scored HLA-DMA profile across patient tissues and cancer cell-line models. HLA-DMA 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, HLA-DMA is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, HLA-DMA protein abundance shows 24,810 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where HLA-DMA 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 HLA-DMA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HLA-DMA survival associations across molecular data types. HLA-DMA RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible HLA-DMA RNA expression–survival associations across cancer types. High HLA-DMA expression shows unfavorable associations in UVM and LGG, but favorable associations in SKCM, KIRC, HNSC and LUAD. 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 HLA-DMA RNA expression.
This table summarizes HLA-DMA 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 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for HLA-DMA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HLA-DMA shows lower tumor expression in LUAD, KICH and LUSC and higher tumor expression in KIRC, LIHC and THCA. The KIRC box plot shows higher HLA-DMA RNA expression in tumor versus normal tissue (log2 FC = +1.677, t-test p < 0.001).
This table shows molecular features associated with HLA-DMA in patient tissues and cancer cell lines. In patient samples, HLA-DMA shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, HLA-DMA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and BLOOD_Leukemia.
