polycomb group ring finger 6Genealiases: MBLR · RNF134
Q-omics provides the consensus-scored PCGF6 profile across patient tissues and cancer cell-line models. PCGF6 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PCGF6 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, PCGF6 RNA expression shows 19,550 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where PCGF6 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 PCGF6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PCGF6 survival associations across molecular data types. PCGF6 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PCGF6 RNA expression–survival associations across cancer types. High PCGF6 expression shows unfavorable associations in ACC, MESO, KIRP, ESCA and LIHC, but favorable associations in LGG. The ACC 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 ACC as the clearest survival context for PCGF6 RNA expression.
This table summarizes PCGF6 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PCGF6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PCGF6 shows lower tumor expression in KICH and higher tumor expression in HNSC, KIRC, LUAD, LUSC and LIHC. The HNSC box plot shows higher PCGF6 RNA expression in tumor versus normal tissue (log2 FC = +0.954, t-test p < 0.001).
This table shows molecular features associated with PCGF6 in patient tissues and cancer cell lines. In patient samples, PCGF6 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, PCGF6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in OVARY and LARGE_INTESTINE.
