Q-omics provides the consensus-scored ARHGEF18 profile across patient tissues and cancer cell-line models. ARHGEF18 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, ARHGEF18 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, ARHGEF18 RNA expression shows 19,072 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight SCLC, KIRC, and ACC as cancer lineages where ARHGEF18 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 ARHGEF18 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ARHGEF18 survival associations across molecular data types. ARHGEF18 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) 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 ARHGEF18 RNA expression–survival associations across cancer types. High ARHGEF18 expression shows unfavorable associations in MESO, LUAD and LUSC, but favorable associations in SCLC, HNSC and KIRC. The SCLC 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 SCLC as the clearest survival context for ARHGEF18 RNA expression.
This table summarizes ARHGEF18 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 6. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for ARHGEF18. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ARHGEF18 shows lower tumor expression in KIRC and COAD and higher tumor expression in HNSC, LIHC, BLCA and BRCA. The KIRC box plot shows higher ARHGEF18 RNA expression in normal versus tumor tissue (log2 FC = −0.660, t-test p < 0.001).
This table shows molecular features associated with ARHGEF18 in patient tissues and cancer cell lines. In patient samples, ARHGEF18 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, ARHGEF18 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in CNS and BLOOD_Lymphoma.
