Q-omics provides the consensus-scored EVC2 profile across patient tissues and cancer cell-line models. EVC2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, EVC2 is differentially expressed in 11, with the highest sampling consensus in THCA. Additionally, EVC2 RNA expression shows 18,599 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight BLCA, THCA, and UVM as cancer lineages where EVC2 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 EVC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EVC2 survival associations across molecular data types. EVC2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (9) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EVC2 RNA expression–survival associations across cancer types. High EVC2 expression shows unfavorable associations in BLCA, LGG, ACC and LIHC, but favorable associations in KIRC and ESCA. The BLCA 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 BLCA as the clearest survival context for EVC2 RNA expression.
This table summarizes EVC2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 1. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EVC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EVC2 shows lower tumor expression in THCA, COAD, UCEC, BLCA, BRCA and KICH. The THCA box plot shows higher EVC2 RNA expression in normal versus tumor tissue (log2 FC = −0.579, t-test p < 0.001).
This table shows molecular features associated with EVC2 in patient tissues and cancer cell lines. In patient samples, EVC2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, EVC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Leukemia.
