Q-omics provides the consensus-scored RGL2 profile across patient tissues and cancer cell-line models. RGL2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, RGL2 is differentially expressed in 11, with the highest sampling consensus in COAD. Additionally, RGL2 protein abundance shows 23,892 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight COAD, and LSCC as cancer lineages where RGL2 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 RGL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RGL2 survival associations across molecular data types. RGL2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (8) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RGL2 RNA expression–survival associations across cancer types. High RGL2 expression shows unfavorable associations in COAD, ACC, LGG and KICH, but favorable associations in THYM and LUAD. 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 RGL2 RNA expression.
This table summarizes RGL2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RGL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RGL2 shows lower tumor expression in KICH and LUSC and higher tumor expression in COAD, LIHC, UCEC and CHOL. The COAD box plot shows higher RGL2 RNA expression in tumor versus normal tissue (log2 FC = +1.315, t-test p < 0.001).
This table shows molecular features associated with RGL2 in patient tissues and cancer cell lines. In patient samples, RGL2 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, RGL2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in OVARY and UPPER_AERODIGESTIVE_TRACT.
