Q-omics provides the consensus-scored ARHGEF17 profile across patient tissues and cancer cell-line models. ARHGEF17 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LUSC. Among the 18 cancer types available for tumor–normal comparison, ARHGEF17 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, ARHGEF17 protein abundance shows 30,109 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight LUSC, HNSC, and LSCC as cancer lineages where ARHGEF17 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 ARHGEF17 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ARHGEF17 survival associations across molecular data types. ARHGEF17 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (6) 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 ARHGEF17 RNA expression–survival associations across cancer types. High ARHGEF17 expression shows unfavorable associations in LUSC, BLCA, OV and COAD, but favorable associations in KIRC and LGG. The LUSC 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 LUSC as the clearest survival context for ARHGEF17 RNA expression.
This table summarizes ARHGEF17 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 9. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ARHGEF17. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ARHGEF17 shows lower tumor expression in BLCA, LUAD and LUSC and higher tumor expression in HNSC, KIRC and LIHC. The HNSC box plot shows higher ARHGEF17 RNA expression in tumor versus normal tissue (log2 FC = +0.975, t-test p < 0.001).
This table shows molecular features associated with ARHGEF17 in patient tissues and cancer cell lines. In patient samples, ARHGEF17 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, ARHGEF17 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and LARGE_INTESTINE.
