Q-omics provides the consensus-scored EPC1 profile across patient tissues and cancer cell-line models. EPC1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, EPC1 is differentially expressed in 12, with the highest sampling consensus in THCA. Additionally, EPC1 protein abundance shows 24,129 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, THCA, and GBM as cancer lineages where EPC1 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 EPC1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPC1 survival associations across molecular data types. EPC1 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (9) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EPC1 RNA expression–survival associations across cancer types. High EPC1 expression shows unfavorable associations in ACC and KIRP, but favorable associations in KIRC, HNSC, SKCM and LGG. 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 EPC1 RNA expression.
This table summarizes EPC1 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 7. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EPC1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPC1 shows lower tumor expression in THCA, KICH, BRCA and LUSC and higher tumor expression in KIRC and LIHC. The THCA box plot shows higher EPC1 RNA expression in normal versus tumor tissue (log2 FC = −0.683, t-test p < 0.001).
This table shows molecular features associated with EPC1 in patient tissues and cancer cell lines. In patient samples, EPC1 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, EPC1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
