Q-omics provides the consensus-scored EGFL8 profile across patient tissues and cancer cell-line models. EGFL8 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, EGFL8 is differentially expressed in 8, with the highest sampling consensus in LIHC. Additionally, EGFL8 RNA expression shows 18,209 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight HNSC, LIHC, and UVM as cancer lineages where 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 EGFL8 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EGFL8 survival associations across molecular data types. EGFL8 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (2) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EGFL8 RNA expression–survival associations across cancer types. High EGFL8 expression shows unfavorable associations in KIRC, COAD, SKCM and LUAD, but favorable associations in HNSC and THYM. 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 EGFL8 RNA expression.
This table summarizes EGFL8 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 3. The strongest signals are observed in LIHC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for EGFL8. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EGFL8 shows lower tumor expression in BRCA, KICH and UCEC and higher tumor expression in LIHC, CHOL and COAD. The LIHC box plot shows higher EGFL8 RNA expression in tumor versus normal tissue (log2 FC = +0.360, t-test p < 0.001).
This table shows molecular features associated with EGFL8 in patient tissues and cancer cell lines. In patient samples, EGFL8 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, EGFL8 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 BLOOD_Leukemia and SKIN.
