RAP1 GTPase activating protein 2Genealiases: GARNL4 · RAP1GA3
Q-omics provides the consensus-scored RAP1GAP2 profile across patient tissues and cancer cell-line models. RAP1GAP2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, RAP1GAP2 is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, RAP1GAP2 protein abundance shows 24,054 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, and GBM as cancer lineages where RAP1GAP2 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 RAP1GAP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RAP1GAP2 survival associations across molecular data types. RAP1GAP2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RAP1GAP2 RNA expression–survival associations across cancer types. High RAP1GAP2 expression shows unfavorable associations in KIRC, ACC, CESC and BRCA, but favorable associations in READ and KIRP. The KIRC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify KIRC as the clearest survival context for RAP1GAP2 RNA expression.
This table summarizes RAP1GAP2 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 7. The strongest signals are observed in KIRC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for RAP1GAP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RAP1GAP2 shows lower tumor expression in KIRC and higher tumor expression in THCA, COAD, CHOL, LIHC and STAD. The KIRC box plot shows higher RAP1GAP2 RNA expression in normal versus tumor tissue (log2 FC = −1.397, t-test p < 0.001).
This table shows molecular features associated with RAP1GAP2 in patient tissues and cancer cell lines. In patient samples, RAP1GAP2 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, RAP1GAP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in STOMACH, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
