Q-omics provides the consensus-scored RAPGEF2 profile across patient tissues and cancer cell-line models. RAPGEF2 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, RAPGEF2 is differentially expressed in 11, with the highest sampling consensus in THCA. Additionally, RAPGEF2 protein abundance shows 29,519 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, THCA, and GBM as cancer lineages where RAPGEF2 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 RAPGEF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RAPGEF2 survival associations across molecular data types. RAPGEF2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (8) 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 RAPGEF2 RNA expression–survival associations across cancer types. High RAPGEF2 expression shows unfavorable associations in LUSC and CESC, but favorable associations in KIRC, HNSC, UCS and SKCM. 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 RAPGEF2 RNA expression.
This table summarizes RAPGEF2 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 THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for RAPGEF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RAPGEF2 shows lower tumor expression in THCA, LUAD, LUSC, BRCA and COAD and higher tumor expression in KIRC. The THCA box plot shows higher RAPGEF2 RNA expression in normal versus tumor tissue (log2 FC = −0.886, t-test p < 0.001).
This table shows molecular features associated with RAPGEF2 in patient tissues and cancer cell lines. In patient samples, RAPGEF2 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, RAPGEF2 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 LIVER and BLOOD_Leukemia.
