Q-omics provides the consensus-scored ARHGAP26 profile across patient tissues and cancer cell-line models. ARHGAP26 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ARHGAP26 is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, ARHGAP26 protein abundance shows 21,502 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, and GBM as cancer lineages where ARHGAP26 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 ARHGAP26 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ARHGAP26 survival associations across molecular data types. ARHGAP26 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (9) 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 ARHGAP26 RNA expression–survival associations across cancer types. High ARHGAP26 expression shows unfavorable associations in KICH and LUAD, but favorable associations in KIRC, HNSC, BLCA and UCEC. 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 ARHGAP26 RNA expression.
This table summarizes ARHGAP26 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 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ARHGAP26. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ARHGAP26 shows lower tumor expression in KICH, THCA and BRCA and higher tumor expression in KIRC, KIRP and LIHC. The KIRC box plot shows higher ARHGAP26 RNA expression in tumor versus normal tissue (log2 FC = +1.179, t-test p < 0.001).
This table shows molecular features associated with ARHGAP26 in patient tissues and cancer cell lines. In patient samples, ARHGAP26 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, ARHGAP26 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and BLOOD_Leukemia.
