Q-omics provides the consensus-scored IRGC profile across patient tissues and cancer cell-line models. IRGC expression is associated with patient survival in 17 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, IRGC is differentially expressed in 5, with the highest sampling consensus in READ. Additionally, IRGC RNA expression shows 8,212 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight COAD, READ, and TGCT as cancer lineages where IRGC 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 IRGC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IRGC survival associations across molecular data types. IRGC RNA expression shows survival associations in the most cancer types (17), followed by mutation status (4) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IRGC RNA expression–survival associations across cancer types. High IRGC expression shows unfavorable associations in COAD, KIRC, ACC, CHOL and BRCA, but favorable associations in UCEC. The COAD 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 COAD as the clearest survival context for IRGC RNA expression.
This table summarizes IRGC 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 1. The strongest signals are observed in READ for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for IRGC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IRGC shows lower tumor expression in READ and BLCA and higher tumor expression in KIRP, KIRC and HNSC. The READ box plot shows higher IRGC RNA expression in normal versus tumor tissue (log2 FC = −0.035, t-test p = .031).
This table shows molecular features associated with IRGC in patient tissues and cancer cell lines. In patient samples, IRGC 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, IRGC RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BREAST and LARGE_INTESTINE.
