Q-omics provides the consensus-scored EFNA2 profile across patient tissues and cancer cell-line models. EFNA2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EFNA2 is differentially expressed in 14, with the highest sampling consensus in LUAD. Additionally, EFNA2 protein abundance shows 33,883 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, LUAD, and GBM as cancer lineages where EFNA2 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 EFNA2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EFNA2 survival associations across molecular data types. EFNA2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (2) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EFNA2 RNA expression–survival associations across cancer types. High EFNA2 expression shows unfavorable associations in ACC, MESO, UVM, KIRP and LUAD, but favorable associations in COAD. The ACC 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 ACC as the clearest survival context for EFNA2 RNA expression.
This table summarizes EFNA2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in LUAD for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EFNA2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EFNA2 shows lower tumor expression in COAD and higher tumor expression in LUAD, KIRP, BRCA, LUSC and ESCA. The LUAD box plot shows higher EFNA2 RNA expression in tumor versus normal tissue (log2 FC = +0.874, t-test p < 0.001).
This table shows molecular features associated with EFNA2 in patient tissues and cancer cell lines. In patient samples, EFNA2 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, EFNA2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BREAST and LUNG_NSCLC_LUAD.
