Q-omics provides the consensus-scored RALGPS2 profile across patient tissues and cancer cell-line models. RALGPS2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, RALGPS2 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, RALGPS2 RNA expression shows 20,153 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRP, KIRC, and UVM as cancer lineages where RALGPS2 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 RALGPS2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RALGPS2 survival associations across molecular data types. RALGPS2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RALGPS2 RNA expression–survival associations across cancer types. High RALGPS2 expression shows unfavorable associations in KIRP, MESO, LUAD and ACC, but favorable associations in UCS and BRCA. The KIRP 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 KIRP as the clearest survival context for RALGPS2 RNA expression.
This table summarizes RALGPS2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for RALGPS2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RALGPS2 shows lower tumor expression in KIRC and UCEC and higher tumor expression in LUAD, LUSC, BLCA and BRCA. The KIRC box plot shows higher RALGPS2 RNA expression in normal versus tumor tissue (log2 FC = −0.752, t-test p < 0.001).
This table shows molecular features associated with RALGPS2 in patient tissues and cancer cell lines. In patient samples, RALGPS2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, RALGPS2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
