Q-omics provides the consensus-scored PPT2-EGFL8 profile across patient tissues and cancer cell-line models. PPT2-EGFL8 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, PPT2-EGFL8 is differentially expressed in 11, with the highest sampling consensus in LIHC. Additionally, PPT2-EGFL8 RNA expression shows 20,092 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight HNSC, LIHC, and ACC as cancer lineages where PPT2-EGFL8 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 PPT2-EGFL8 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPT2-EGFL8 survival associations across molecular data types. PPT2-EGFL8 RNA expression shows survival associations in the most cancer types (23). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PPT2-EGFL8 RNA expression–survival associations across cancer types. High PPT2-EGFL8 expression shows unfavorable associations in ACC, KIRC and COAD, but favorable associations in HNSC, PAAD and READ. The HNSC 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 HNSC as the clearest survival context for PPT2-EGFL8 RNA expression.
This table summarizes PPT2-EGFL8 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for PPT2-EGFL8. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPT2-EGFL8 shows lower tumor expression in KICH and higher tumor expression in LIHC, COAD, KIRC, CHOL and LUSC. The LIHC box plot shows higher PPT2-EGFL8 RNA expression in tumor versus normal tissue (log2 FC = +0.316, t-test p < 0.001).
This table shows molecular features associated with PPT2-EGFL8 in patient tissues and cancer cell lines. In patient samples, PPT2-EGFL8 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, PPT2-EGFL8 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST.
