Q-omics provides the consensus-scored CCNE1 profile across patient tissues and cancer cell-line models. CCNE1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, CCNE1 is differentially expressed in 16, with the highest sampling consensus in BLCA. Additionally, CCNE1 RNA expression shows 21,014 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight KIRP, BLCA, and BRCA as cancer lineages where CCNE1 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 CCNE1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CCNE1 survival associations across molecular data types. CCNE1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CCNE1 RNA expression–survival associations across cancer types. High CCNE1 expression shows unfavorable associations in KIRP, KIRC, BRCA, MESO, ACC and KICH. 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 CCNE1 RNA expression.
This table summarizes CCNE1 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 3. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for CCNE1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CCNE1 shows higher tumor expression in BLCA, COAD, HNSC, LUAD, KIRP and KIRC. The BLCA box plot shows higher CCNE1 RNA expression in tumor versus normal tissue (log2 FC = +2.120, t-test p < 0.001).
This table shows molecular features associated with CCNE1 in patient tissues and cancer cell lines. In patient samples, CCNE1 shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, CCNE1 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 SKIN.
