Q-omics provides the consensus-scored PGAP6 profile across patient tissues and cancer cell-line models. PGAP6 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PGAP6 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, PGAP6 RNA expression shows 18,801 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KIRC as cancer lineages where PGAP6 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 PGAP6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGAP6 survival associations across molecular data types. PGAP6 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (2) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PGAP6 RNA expression–survival associations across cancer types. High PGAP6 expression shows unfavorable associations in ACC, KIRC, UVM, LGG, LUSC and BLCA. 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 PGAP6 RNA expression.
This table summarizes PGAP6 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PGAP6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGAP6 shows lower tumor expression in KIRC and higher tumor expression in BLCA, LIHC, LUAD, THCA and STAD. The KIRC box plot shows higher PGAP6 RNA expression in normal versus tumor tissue (log2 FC = −0.756, t-test p < 0.001).
This table shows molecular features associated with PGAP6 in patient tissues and cancer cell lines. In patient samples, PGAP6 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, PGAP6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and SOFT_TISSUE.
