Q-omics provides the consensus-scored ENO2 profile across patient tissues and cancer cell-line models. ENO2 expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ENO2 is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, ENO2 protein abundance shows 26,155 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight MESO, KIRC, and GBM as cancer lineages where ENO2 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 ENO2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ENO2 survival associations across molecular data types. ENO2 RNA expression shows survival associations in the most cancer types (29), followed by mutation status (7) 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 ENO2 RNA expression–survival associations across cancer types. High ENO2 expression shows unfavorable associations in MESO, KIRP, KICH, THCA and COAD, but favorable associations in LGG. The MESO 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 MESO as the clearest survival context for ENO2 RNA expression.
This table summarizes ENO2 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 8. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ENO2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ENO2 shows lower tumor expression in KICH and higher tumor expression in KIRC, HNSC, KIRP, BRCA and LUSC. The KIRC box plot shows higher ENO2 RNA expression in tumor versus normal tissue (log2 FC = +3.233, t-test p < 0.001).
This table shows molecular features associated with ENO2 in patient tissues and cancer cell lines. In patient samples, ENO2 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, ENO2 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 CNS and LARGE_INTESTINE.
