Q-omics provides the consensus-scored GLRA2 profile across patient tissues and cancer cell-line models. GLRA2 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, GLRA2 is differentially expressed in 5, with the highest sampling consensus in KICH. Additionally, GLRA2 RNA expression shows 13,952 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRP, KICH, and TGCT as cancer lineages where GLRA2 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 GLRA2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GLRA2 survival associations across molecular data types. GLRA2 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (5) 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 GLRA2 RNA expression–survival associations across cancer types. High GLRA2 expression shows unfavorable associations in KIRP and ACC, but favorable associations in SCLC, KIRC, CHOL and UCS. The KIRP Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .003). Together, the overview and detailed table identify KIRP as the clearest survival context for GLRA2 RNA expression.
This table summarizes GLRA2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for GLRA2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GLRA2 shows lower tumor expression in KICH, READ and COAD and higher tumor expression in HNSC and LUSC. The KICH box plot shows higher GLRA2 RNA expression in normal versus tumor tissue (log2 FC = −0.085, t-test p = .022).
This table shows molecular features associated with GLRA2 in patient tissues and cancer cell lines. In patient samples, GLRA2 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, GLRA2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
