Q-omics provides the consensus-scored HEBP2 profile across patient tissues and cancer cell-line models. HEBP2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ESCA. Among the 18 cancer types available for tumor–normal comparison, HEBP2 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, HEBP2 protein abundance shows 20,538 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ESCA, KICH, and PDAC as cancer lineages where HEBP2 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 HEBP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HEBP2 survival associations across molecular data types. HEBP2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (1) 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 HEBP2 RNA expression–survival associations across cancer types. High HEBP2 expression shows unfavorable associations in ESCA, LUAD, OV, LGG and UCEC, but favorable associations in BLCA. The ESCA 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 ESCA as the clearest survival context for HEBP2 RNA expression.
This table summarizes HEBP2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 5. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for HEBP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HEBP2 shows lower tumor expression in KICH, BRCA and HNSC and higher tumor expression in LUAD, LUSC and COAD. The KICH box plot shows higher HEBP2 RNA expression in normal versus tumor tissue (log2 FC = −1.194, t-test p < 0.001).
This table shows molecular features associated with HEBP2 in patient tissues and cancer cell lines. In patient samples, HEBP2 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, HEBP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BONE.
