Q-omics provides the consensus-scored ERBB2 profile across patient tissues and cancer cell-line models. ERBB2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ERBB2 is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, ERBB2 RNA expression shows 19,809 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRC, and THYM as cancer lineages where ERBB2 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 ERBB2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ERBB2 survival associations across molecular data types. ERBB2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (10) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ERBB2 RNA expression–survival associations across cancer types. High ERBB2 expression shows unfavorable associations in OV, ACC and LGG, but favorable associations in KIRC, KICH and UCS. The KIRC 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 KIRC as the clearest survival context for ERBB2 RNA expression.
This table summarizes ERBB2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ERBB2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ERBB2 shows lower tumor expression in KIRC, HNSC and KICH and higher tumor expression in STAD, LUAD and BRCA. The KIRC box plot shows higher ERBB2 RNA expression in normal versus tumor tissue (log2 FC = −1.318, t-test p < 0.001).
This table shows molecular features associated with ERBB2 in patient tissues and cancer cell lines. In patient samples, ERBB2 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, ERBB2 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 BLOOD_Leukemia.
