Q-omics provides the consensus-scored CPLX3 profile across patient tissues and cancer cell-line models. CPLX3 expression is associated with patient survival in 9 of 34 cancer types, with the highest sampling consensus in COAD. Additionally, CPLX3 protein abundance shows 7,990 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight COAD, and GBM as cancer lineages where CPLX3 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 CPLX3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CPLX3 survival associations across molecular data types. CPLX3 RNA expression shows survival associations in the most cancer types (9), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CPLX3 RNA expression–survival associations across cancer types. High CPLX3 expression shows unfavorable associations in COAD, KIRC, BLCA, UCEC, TGCT and ACC. The COAD 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 COAD as the clearest survival context for CPLX3 RNA expression.
This table shows molecular features associated with CPLX3 in patient tissues and cancer cell lines. In patient samples, CPLX3 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, CPLX3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Lymphoma.
