Q-omics provides the consensus-scored RERGL profile across patient tissues and cancer cell-line models. RERGL expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, RERGL is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, RERGL RNA expression shows 15,899 significant protein co-abundance associations, with the highest sampling consensus in CCRCC. Together, these results highlight ACC, KIRC, and CCRCC as cancer lineages where RERGL 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 RERGL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RERGL survival associations across molecular data types. RERGL RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RERGL RNA expression–survival associations across cancer types. High RERGL expression shows unfavorable associations in ACC and BLCA, but favorable associations in KIRC, LGG, LIHC and ESCA. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify ACC as the clearest survival context for RERGL RNA expression.
This table summarizes RERGL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for RERGL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RERGL shows lower tumor expression in KIRC, KICH, KIRP, THCA, COAD and BLCA. The KIRC box plot shows higher RERGL RNA expression in normal versus tumor tissue (log2 FC = −2.092, t-test p < 0.001).
This table shows molecular features associated with RERGL in patient tissues and cancer cell lines. In patient samples, RERGL shows the broadest associations at the RNA and protein expression levels, with CCRCC recurring as the lineage with the largest associated feature set. In cancer cell lines, RERGL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and UPPER_AERODIGESTIVE_TRACT.
