Q-omics provides the consensus-scored RNASEH2A profile across patient tissues and cancer cell-line models. RNASEH2A expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, RNASEH2A is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, RNASEH2A protein abundance shows 27,248 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where RNASEH2A 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 RNASEH2A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RNASEH2A survival associations across molecular data types. RNASEH2A RNA expression shows survival associations in the most cancer types (27), followed by mutation status (3) and mass-spec protein abundance (13). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RNASEH2A RNA expression–survival associations across cancer types. High RNASEH2A expression shows unfavorable associations in ACC, MESO, KICH, KIRP and LIHC, but favorable associations in CESC. 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 RNASEH2A RNA expression.
This table summarizes RNASEH2A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 9. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RNASEH2A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RNASEH2A shows higher tumor expression in HNSC, KIRC, COAD, BLCA, KIRP and LUAD. The HNSC box plot shows higher RNASEH2A RNA expression in tumor versus normal tissue (log2 FC = +1.853, t-test p < 0.001).
This table shows molecular features associated with RNASEH2A in patient tissues and cancer cell lines. In patient samples, RNASEH2A shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, RNASEH2A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
