Q-omics provides the consensus-scored RNF6 profile across patient tissues and cancer cell-line models. RNF6 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, RNF6 is differentially expressed in 15, with the highest sampling consensus in STAD. Additionally, RNF6 RNA expression shows 20,333 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight CESC, STAD, and UVM as cancer lineages where RNF6 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 RNF6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RNF6 survival associations across molecular data types. RNF6 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) 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 RNF6 RNA expression–survival associations across cancer types. High RNF6 expression shows unfavorable associations in CESC, ESCA, UVM and HNSC, but favorable associations in KIRC and UCEC. The CESC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify CESC as the clearest survival context for RNF6 RNA expression.
This table summarizes RNF6 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 6. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for RNF6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RNF6 shows lower tumor expression in THCA and KICH and higher tumor expression in STAD, BLCA, LIHC and HNSC. The STAD box plot shows higher RNF6 RNA expression in tumor versus normal tissue (log2 FC = +0.912, t-test p < 0.001).
This table shows molecular features associated with RNF6 in patient tissues and cancer cell lines. In patient samples, RNF6 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, RNF6 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 BREAST and BLOOD_Leukemia.
