Q-omics provides the consensus-scored HLA-C profile across patient tissues and cancer cell-line models. HLA-C expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, HLA-C is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, HLA-C RNA expression shows 16,280 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight SKCM, HNSC, and UVM as cancer lineages where HLA-C 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-C — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HLA-C survival associations across molecular data types. HLA-C RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3) 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 HLA-C RNA expression–survival associations across cancer types. High HLA-C expression shows unfavorable associations in UVM, THYM, LGG and LUSC, but favorable associations in SKCM and CESC. 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-C RNA expression.
This table summarizes HLA-C tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for HLA-C. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HLA-C shows lower tumor expression in LUAD and LUSC and higher tumor expression in HNSC, KIRC, KIRP and THCA. The HNSC box plot shows higher HLA-C RNA expression in tumor versus normal tissue (log2 FC = +1.668, t-test p < 0.001).
This table shows molecular features associated with HLA-C in patient tissues and cancer cell lines. In patient samples, HLA-C shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, HLA-C RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in CNS and SOFT_TISSUE.
