Q-omics provides the consensus-scored CCND2P1 profile across patient tissues and cancer cell-line models. CCND2P1 expression is associated with patient survival in 16 of 34 cancer types, with the highest sampling consensus in OV. Among the 18 cancer types available for tumor–normal comparison, CCND2P1 is differentially expressed in 5, with the highest sampling consensus in LIHC. Additionally, CCND2P1 RNA expression shows 9,550 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight OV, LIHC, and GBM as cancer lineages where CCND2P1 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 CCND2P1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CCND2P1 survival associations across molecular data types. CCND2P1 RNA expression shows survival associations in the most cancer types (16). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CCND2P1 RNA expression–survival associations across cancer types. High CCND2P1 expression shows unfavorable associations in OV, BLCA, CHOL, HNSC and DLBC, but favorable associations in LIHC. The OV 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 OV as the clearest survival context for CCND2P1 RNA expression.
This table summarizes CCND2P1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for CCND2P1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CCND2P1 shows lower tumor expression in LIHC and CHOL and higher tumor expression in BRCA, HNSC and LUSC. The LIHC box plot shows higher CCND2P1 RNA expression in normal versus tumor tissue (log2 FC = −1.977, t-test p < 0.001).
This table shows molecular features associated with CCND2P1 in patient tissues and cancer cell lines. In patient samples, CCND2P1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set.
