Q-omics provides the consensus-scored HLA-DQA2 profile across patient tissues and cancer cell-line models. HLA-DQA2 expression is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, HLA-DQA2 is differentially expressed in 5, with the highest sampling consensus in KIRC. Additionally, HLA-DQA2 RNA expression shows 9,806 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight SKCM, KIRC, and TGCT as cancer lineages where HLA-DQA2 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-DQA2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HLA-DQA2 survival associations across molecular data types. HLA-DQA2 RNA expression shows survival associations in the most cancer types (30), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible HLA-DQA2 RNA expression–survival associations across cancer types. High HLA-DQA2 expression shows unfavorable associations in UVM, but favorable associations in SKCM, KIRC, CESC, HNSC and ACC. 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-DQA2 RNA expression.
This table summarizes HLA-DQA2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for HLA-DQA2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HLA-DQA2 shows lower tumor expression in LUSC and PAAD and higher tumor expression in KIRC, THCA and BRCA. The KIRC box plot shows higher HLA-DQA2 RNA expression in tumor versus normal tissue (log2 FC = +2.530, t-test p < 0.001).
This table shows molecular features associated with HLA-DQA2 in patient tissues and cancer cell lines. In patient samples, HLA-DQA2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, HLA-DQA2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in STOMACH, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BREAST.
