Q-omics provides the consensus-scored E2F3 profile across patient tissues and cancer cell-line models. E2F3 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, E2F3 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, E2F3 protein abundance shows 23,286 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, HNSC, and GBM as cancer lineages where E2F3 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 E2F3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes E2F3 survival associations across molecular data types. E2F3 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) 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 E2F3 RNA expression–survival associations across cancer types. High E2F3 expression shows unfavorable associations in KIRP, MESO, LIHC, ACC and SARC, but favorable associations in BLCA. The KIRP 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 KIRP as the clearest survival context for E2F3 RNA expression.
This table summarizes E2F3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for E2F3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. E2F3 shows higher tumor expression in HNSC, BLCA, COAD, STAD, KIRC and LUAD. The HNSC box plot shows higher E2F3 RNA expression in tumor versus normal tissue (log2 FC = +1.190, t-test p < 0.001).
This table shows molecular features associated with E2F3 in patient tissues and cancer cell lines. In patient samples, E2F3 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, E2F3 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 SOFT_TISSUE and BLOOD_Leukemia.
