Q-omics provides the consensus-scored EAPP profile across patient tissues and cancer cell-line models. EAPP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in BRCA. Among the 18 cancer types available for tumor–normal comparison, EAPP is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, EAPP protein abundance shows 19,282 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight BRCA, KICH, and LSCC as cancer lineages where EAPP 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 EAPP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EAPP survival associations across molecular data types. EAPP RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EAPP RNA expression–survival associations across cancer types. High EAPP expression shows unfavorable associations in UVM, SCLC and ACC, but favorable associations in BRCA, KIRC and MESO. The BRCA 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 BRCA as the clearest survival context for EAPP RNA expression.
This table summarizes EAPP 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 5. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EAPP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EAPP shows lower tumor expression in KICH, THCA, COAD, READ and UCEC and higher tumor expression in LIHC. The KICH box plot shows higher EAPP RNA expression in normal versus tumor tissue (log2 FC = −1.047, t-test p < 0.001).
This table shows molecular features associated with EAPP in patient tissues and cancer cell lines. In patient samples, EAPP shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, EAPP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and BLOOD_Leukemia.
