Q-omics provides the consensus-scored GPX6 profile across patient tissues and cancer cell-line models. GPX6 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, GPX6 is differentially expressed in 4, with the highest sampling consensus in HNSC. Additionally, GPX6 RNA expression shows 6,314 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight UCS, HNSC, and STAD as cancer lineages where GPX6 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 GPX6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GPX6 survival associations across molecular data types. GPX6 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GPX6 RNA expression–survival associations across cancer types. High GPX6 expression shows unfavorable associations in UCS, KIRC, KICH, PAAD and READ, but favorable associations in CESC. The UCS 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 UCS as the clearest survival context for GPX6 RNA expression.
This table summarizes GPX6 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 4. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for GPX6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GPX6 shows lower tumor expression in PRAD and higher tumor expression in HNSC, LIHC and KIRC. The HNSC box plot shows higher GPX6 RNA expression in tumor versus normal tissue (log2 FC = +0.014, t-test p = .029).
This table shows molecular features associated with GPX6 in patient tissues and cancer cell lines. In patient samples, GPX6 shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set. In cancer cell lines, GPX6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in OVARY and SKIN.
