Q-omics provides the consensus-scored AEBP1 profile across patient tissues and cancer cell-line models. AEBP1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, AEBP1 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, AEBP1 protein abundance shows 27,950 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, HNSC, and GBM as cancer lineages where AEBP1 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 AEBP1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AEBP1 survival associations across molecular data types. AEBP1 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (5) 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 AEBP1 RNA expression–survival associations across cancer types. High AEBP1 expression shows unfavorable associations in KIRP, LGG, MESO, BLCA, ACC and GBM. The KIRP 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 KIRP as the clearest survival context for AEBP1 RNA expression.
This table summarizes AEBP1 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 7. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for AEBP1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AEBP1 shows lower tumor expression in KICH and UCEC and higher tumor expression in HNSC, BRCA, LUAD and STAD. The HNSC box plot shows higher AEBP1 RNA expression in tumor versus normal tissue (log2 FC = +3.875, t-test p < 0.001).
This table shows molecular features associated with AEBP1 in patient tissues and cancer cell lines. In patient samples, AEBP1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, AEBP1 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 LUNG_NSCLC_LUAD and BLOOD_Leukemia.
