Q-omics provides the consensus-scored EVL profile across patient tissues and cancer cell-line models. EVL expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, EVL is differentially expressed in 8, with the highest sampling consensus in KIRC. Additionally, EVL protein abundance shows 23,877 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where EVL 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 EVL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EVL survival associations across molecular data types. EVL RNA expression shows survival associations in the most cancer types (27), followed by mutation status (3) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EVL RNA expression–survival associations across cancer types. High EVL expression shows unfavorable associations in KIRC and ACC, but favorable associations in SKCM, PAAD, BRCA and HNSC. The SKCM 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 SKCM as the clearest survival context for EVL RNA expression.
This table summarizes EVL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EVL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EVL shows lower tumor expression in LUAD, COAD and LUSC and higher tumor expression in KIRC, KIRP and BRCA. The KIRC box plot shows higher EVL RNA expression in tumor versus normal tissue (log2 FC = +1.501, t-test p < 0.001).
This table shows molecular features associated with EVL in patient tissues and cancer cell lines. In patient samples, EVL 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, EVL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in CNS and BLOOD_Leukemia.
