Q-omics provides the consensus-scored ARHGEF10L profile across patient tissues and cancer cell-line models. ARHGEF10L expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, ARHGEF10L is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, ARHGEF10L protein abundance shows 27,405 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight HNSC as cancer lineages where ARHGEF10L 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 ARHGEF10L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ARHGEF10L survival associations across molecular data types. ARHGEF10L RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) 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 ARHGEF10L RNA expression–survival associations across cancer types. High ARHGEF10L expression shows unfavorable associations in LUSC, but favorable associations in HNSC, MESO, KIRP, SCLC and KIRC. The HNSC 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 HNSC as the clearest survival context for ARHGEF10L RNA expression.
This table summarizes ARHGEF10L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ARHGEF10L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ARHGEF10L shows lower tumor expression in HNSC, KICH, THCA, KIRP, BRCA and KIRC. The HNSC box plot shows higher ARHGEF10L RNA expression in normal versus tumor tissue (log2 FC = −1.602, t-test p < 0.001).
This table shows molecular features associated with ARHGEF10L in patient tissues and cancer cell lines. In patient samples, ARHGEF10L shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, ARHGEF10L 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 UPPER_AERODIGESTIVE_TRACT and CNS.
