Q-omics provides the consensus-scored HBEGF profile across patient tissues and cancer cell-line models. HBEGF expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, HBEGF is differentially expressed in 9, with the highest sampling consensus in KIRP. Additionally, HBEGF protein abundance shows 21,840 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight HNSC, KIRP, and PDAC as cancer lineages where HBEGF 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 HBEGF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HBEGF survival associations across molecular data types. HBEGF RNA expression shows survival associations in the most cancer types (19), followed by mutation status (3) 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 HBEGF RNA expression–survival associations across cancer types. High HBEGF expression shows unfavorable associations in HNSC, MESO, UVM, PAAD, KIRP and LGG. The HNSC 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 HNSC as the clearest survival context for HBEGF RNA expression.
This table summarizes HBEGF tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRP for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for HBEGF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HBEGF shows lower tumor expression in KIRP, KICH, LUSC, LUAD, BLCA and KIRC. The KIRP box plot shows higher HBEGF RNA expression in normal versus tumor tissue (log2 FC = −2.345, t-test p < 0.001).
This table shows molecular features associated with HBEGF in patient tissues and cancer cell lines. In patient samples, HBEGF shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, HBEGF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BONE.
