Q-omics provides the consensus-scored PLA2G2D profile across patient tissues and cancer cell-line models. PLA2G2D expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, PLA2G2D is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, PLA2G2D RNA expression shows 13,056 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where PLA2G2D 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 PLA2G2D — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PLA2G2D survival associations across molecular data types. PLA2G2D RNA expression shows survival associations in the most cancer types (28), followed by mutation status (3) 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 PLA2G2D RNA expression–survival associations across cancer types. High PLA2G2D expression shows favorable associations in SKCM, HNSC, CESC, OV, DLBC and UCEC. The SKCM 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 SKCM as the clearest survival context for PLA2G2D RNA expression.
This table summarizes PLA2G2D tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for PLA2G2D. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PLA2G2D shows lower tumor expression in COAD and higher tumor expression in KIRC, KIRP, LUAD, STAD and BRCA. The KIRC box plot shows higher PLA2G2D RNA expression in tumor versus normal tissue (log2 FC = +1.429, t-test p < 0.001).
This table shows molecular features associated with PLA2G2D in patient tissues and cancer cell lines. In patient samples, PLA2G2D shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, PLA2G2D 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 LARGE_INTESTINE and LUNG_NSCLC_LUSC.
