Q-omics provides the consensus-scored EGFR profile across patient tissues and cancer cell-line models. EGFR expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, EGFR is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, EGFR RNA expression shows 19,671 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight BLCA, KIRC, and THYM as cancer lineages where EGFR 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 EGFR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EGFR survival associations across molecular data types. EGFR RNA expression shows survival associations in the most cancer types (24), followed by mutation status (8) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EGFR RNA expression–survival associations across cancer types. High EGFR expression shows unfavorable associations in BLCA, PAAD, UVM and SKCM, but favorable associations in KIRC and ESCA. The BLCA 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 BLCA as the clearest survival context for EGFR RNA expression.
This table summarizes EGFR tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EGFR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EGFR shows lower tumor expression in COAD and BRCA and higher tumor expression in KIRC, HNSC, KIRP and LUSC. The KIRC box plot shows higher EGFR RNA expression in tumor versus normal tissue (log2 FC = +1.911, t-test p < 0.001).
This table shows molecular features associated with EGFR in patient tissues and cancer cell lines. In patient samples, EGFR shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, EGFR 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 PANCREAS and BONE.
