immunity related GTPase QGenealiases: FKSG27 · IRGQ1
Q-omics provides the consensus-scored IRGQ profile across patient tissues and cancer cell-line models. IRGQ expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, IRGQ is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, IRGQ RNA expression shows 19,770 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight MESO, HNSC, and ACC as cancer lineages where IRGQ 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 IRGQ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IRGQ survival associations across molecular data types. IRGQ RNA expression shows survival associations in the most cancer types (21), followed by mutation status (6) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IRGQ RNA expression–survival associations across cancer types. High IRGQ expression shows unfavorable associations in MESO, LIHC, LGG and ACC, but favorable associations in PAAD and HNSC. The MESO 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 MESO as the clearest survival context for IRGQ RNA expression.
This table summarizes IRGQ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for IRGQ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IRGQ shows higher tumor expression in HNSC, LIHC, COAD, LUSC, CHOL and STAD. The HNSC box plot shows higher IRGQ RNA expression in tumor versus normal tissue (log2 FC = +0.605, t-test p < 0.001).
This table shows molecular features associated with IRGQ in patient tissues and cancer cell lines. In patient samples, IRGQ shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, IRGQ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in OVARY and LARGE_INTESTINE.
