Q-omics provides the consensus-scored CDC42BPG profile across patient tissues and cancer cell-line models. CDC42BPG expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, CDC42BPG is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, CDC42BPG RNA expression shows 19,883 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRP, KIRC, and THYM as cancer lineages where CDC42BPG 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 CDC42BPG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CDC42BPG survival associations across molecular data types. CDC42BPG RNA expression shows survival associations in the most cancer types (24), followed by mutation status (10) 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 CDC42BPG RNA expression–survival associations across cancer types. High CDC42BPG expression shows unfavorable associations in LGG and SKCM, but favorable associations in KIRP, KIRC, UVM and SCLC. The KIRP 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 KIRP as the clearest survival context for CDC42BPG RNA expression.
This table summarizes CDC42BPG 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 THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CDC42BPG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CDC42BPG shows lower tumor expression in KIRC and COAD and higher tumor expression in THCA, BLCA, UCEC and CHOL. The KIRC box plot shows higher CDC42BPG RNA expression in normal versus tumor tissue (log2 FC = −1.112, t-test p < 0.001).
This table shows molecular features associated with CDC42BPG in patient tissues and cancer cell lines. In patient samples, CDC42BPG shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, CDC42BPG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BONE.
