Q-omics provides the consensus-scored RRAGC profile across patient tissues and cancer cell-line models. RRAGC expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, RRAGC is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, RRAGC protein abundance shows 37,812 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight LIHC, HNSC, and LSCC as cancer lineages where RRAGC 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 RRAGC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RRAGC survival associations across molecular data types. RRAGC RNA expression shows survival associations in the most cancer types (23), followed by mutation status (1) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RRAGC RNA expression–survival associations across cancer types. High RRAGC expression shows unfavorable associations in LIHC, ACC, KICH, LGG and STAD, but favorable associations in KIRC. The LIHC 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 LIHC as the clearest survival context for RRAGC RNA expression.
This table summarizes RRAGC 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 14. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for RRAGC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RRAGC shows lower tumor expression in COAD and KICH and higher tumor expression in HNSC, KIRC, KIRP and LIHC. The HNSC box plot shows higher RRAGC RNA expression in tumor versus normal tissue (log2 FC = +0.806, t-test p < 0.001).
This table shows molecular features associated with RRAGC in patient tissues and cancer cell lines. In patient samples, RRAGC shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, RRAGC RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and BLOOD_Lymphoma.
