Q-omics provides the consensus-scored PEG3 profile across patient tissues and cancer cell-line models. PEG3 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PEG3 is differentially expressed in 15, with the highest sampling consensus in BLCA. Additionally, PEG3 RNA expression shows 20,264 significant protein co-abundance associations, with the highest sampling consensus in UCEC. Together, these results highlight KIRC, BLCA, and UCEC as cancer lineages where PEG3 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 PEG3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PEG3 survival associations across molecular data types. PEG3 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (11) 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 PEG3 RNA expression–survival associations across cancer types. High PEG3 expression shows unfavorable associations in LUSC and BLCA, but favorable associations in KIRC, UVM, LUAD and SKCM. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for PEG3 RNA expression.
This table summarizes PEG3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for PEG3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PEG3 shows lower tumor expression in BLCA, THCA, KIRC, HNSC, COAD and KICH. The BLCA box plot shows higher PEG3 RNA expression in normal versus tumor tissue (log2 FC = −2.620, t-test p < 0.001).
This table shows molecular features associated with PEG3 in patient tissues and cancer cell lines. In patient samples, PEG3 shows the broadest associations at the RNA and protein expression levels, with UCEC recurring as the lineage with the largest associated feature set. In cancer cell lines, PEG3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and SOFT_TISSUE.
