Q-omics provides the consensus-scored ARFGEF2 profile across patient tissues and cancer cell-line models. ARFGEF2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ARFGEF2 is differentially expressed in 13, with the highest sampling consensus in STAD. Additionally, ARFGEF2 protein abundance shows 25,896 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, STAD, and GBM as cancer lineages where ARFGEF2 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 ARFGEF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ARFGEF2 survival associations across molecular data types. ARFGEF2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (8) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ARFGEF2 RNA expression–survival associations across cancer types. High ARFGEF2 expression shows unfavorable associations in UVM, MESO, LUSC and LIHC, but favorable associations in KIRC and SCLC. 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 ARFGEF2 RNA expression.
This table summarizes ARFGEF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ARFGEF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ARFGEF2 shows lower tumor expression in KIRC and higher tumor expression in STAD, LIHC, BRCA, HNSC and CHOL. The STAD box plot shows higher ARFGEF2 RNA expression in tumor versus normal tissue (log2 FC = +1.004, t-test p < 0.001).
This table shows molecular features associated with ARFGEF2 in patient tissues and cancer cell lines. In patient samples, ARFGEF2 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, ARFGEF2 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 PANCREAS and UPPER_AERODIGESTIVE_TRACT.
