Q-omics provides the consensus-scored RGL1 profile across patient tissues and cancer cell-line models. RGL1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, RGL1 is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, RGL1 RNA expression shows 19,793 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRC, KICH, and THYM as cancer lineages where RGL1 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 RGL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RGL1 survival associations across molecular data types. RGL1 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (10) 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 RGL1 RNA expression–survival associations across cancer types. High RGL1 expression shows unfavorable associations in UVM, but favorable associations in KIRC, HNSC, MESO, LUAD and UCS. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for RGL1 RNA expression.
This table summarizes RGL1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RGL1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RGL1 shows lower tumor expression in KICH, LUAD, BLCA, LUSC and COAD and higher tumor expression in KIRC. The KICH box plot shows higher RGL1 RNA expression in normal versus tumor tissue (log2 FC = −2.521, t-test p < 0.001).
This table shows molecular features associated with RGL1 in patient tissues and cancer cell lines. In patient samples, RGL1 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, RGL1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and BLOOD_Leukemia.
