Q-omics provides the consensus-scored CCNA2 profile across patient tissues and cancer cell-line models. CCNA2 expression is associated with patient survival in 31 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, CCNA2 is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, CCNA2 RNA expression shows 23,679 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRP, HNSC, and LSCC as cancer lineages where CCNA2 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 CCNA2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CCNA2 survival associations across molecular data types. CCNA2 RNA expression shows survival associations in the most cancer types (31), followed by mutation status (2) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CCNA2 RNA expression–survival associations across cancer types. High CCNA2 expression shows unfavorable associations in KIRP, MESO, ACC, LUAD, LIHC 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 CCNA2 RNA expression.
This table summarizes CCNA2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CCNA2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CCNA2 shows higher tumor expression in HNSC, LUAD, BLCA, KIRP, KIRC and COAD. The HNSC box plot shows higher CCNA2 RNA expression in tumor versus normal tissue (log2 FC = +2.043, t-test p < 0.001).
This table shows molecular features associated with CCNA2 in patient tissues and cancer cell lines. In patient samples, CCNA2 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, CCNA2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Leukemia.
