Q-omics provides the consensus-scored GPX3 profile across patient tissues and cancer cell-line models. GPX3 expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, GPX3 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, GPX3 protein abundance shows 30,297 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight STAD, HNSC, and PDAC as cancer lineages where GPX3 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 GPX3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GPX3 survival associations across molecular data types. GPX3 RNA expression shows survival associations in the most cancer types (29), followed by mutation status (1) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GPX3 RNA expression–survival associations across cancer types. High GPX3 expression shows unfavorable associations in STAD, LUSC and COAD, but favorable associations in KIRC, LGG and PAAD. The STAD 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 STAD as the clearest survival context for GPX3 RNA expression.
This table summarizes GPX3 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 7. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GPX3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GPX3 shows lower tumor expression in HNSC, BLCA, KIRP, COAD, STAD and LUSC. The HNSC box plot shows higher GPX3 RNA expression in normal versus tumor tissue (log2 FC = −2.696, t-test p < 0.001).
This table shows molecular features associated with GPX3 in patient tissues and cancer cell lines. In patient samples, GPX3 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, GPX3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BONE.
