Q-omics provides the consensus-scored EXOC6 profile across patient tissues and cancer cell-line models. EXOC6 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, EXOC6 is differentially expressed in 12, with the highest sampling consensus in LUSC. Additionally, EXOC6 RNA expression shows 21,307 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, LUSC, and UVM as cancer lineages where EXOC6 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 EXOC6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EXOC6 survival associations across molecular data types. EXOC6 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EXOC6 RNA expression–survival associations across cancer types. High EXOC6 expression shows unfavorable associations in UVM, but favorable associations in KIRC, SKCM, LGG, UCS and LUAD. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .003). Together, the overview and detailed table identify KIRC as the clearest survival context for EXOC6 RNA expression.
This table summarizes EXOC6 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 5. The strongest signals are observed in LUSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for EXOC6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EXOC6 shows lower tumor expression in LUSC and THCA and higher tumor expression in LIHC, STAD, KIRP and BRCA. The LUSC box plot shows higher EXOC6 RNA expression in normal versus tumor tissue (log2 FC = −1.334, t-test p < 0.001).
This table shows molecular features associated with EXOC6 in patient tissues and cancer cell lines. In patient samples, EXOC6 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, EXOC6 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 BLOOD_Leukemia and BONE.
