Q-omics provides the consensus-scored EGF profile across patient tissues and cancer cell-line models. EGF expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, EGF is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, EGF RNA expression shows 16,878 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight STAD, KIRC, and UVM as cancer lineages where EGF 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 EGF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EGF survival associations across molecular data types. EGF RNA expression shows survival associations in the most cancer types (23), followed by mutation status (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EGF RNA expression–survival associations across cancer types. High EGF expression shows unfavorable associations in STAD, UVM, BLCA, LIHC, LGG and THCA. 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 EGF RNA expression.
This table summarizes EGF tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for EGF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EGF shows lower tumor expression in KIRC, KIRP, THCA and BRCA and higher tumor expression in LUAD and LIHC. The KIRC box plot shows higher EGF RNA expression in normal versus tumor tissue (log2 FC = −5.129, t-test p < 0.001).
This table shows molecular features associated with EGF in patient tissues and cancer cell lines. In patient samples, EGF 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, EGF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in SKIN and LARGE_INTESTINE.
