Q-omics provides the consensus-scored CLPS profile across patient tissues and cancer cell-line models. CLPS expression is associated with patient survival in 18 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, CLPS is differentially expressed in 7, with the highest sampling consensus in BRCA. Additionally, CLPS RNA expression shows 9,321 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRC, BRCA, and PDAC as cancer lineages where CLPS 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 CLPS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CLPS survival associations across molecular data types. CLPS RNA expression shows survival associations in the most cancer types (18), followed by mutation status (4) 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 CLPS RNA expression–survival associations across cancer types. High CLPS expression shows unfavorable associations in KIRC, LIHC and SARC, but favorable associations in OV, BLCA and UCS. The KIRC 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 KIRC as the clearest survival context for CLPS RNA expression.
This table summarizes CLPS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 5. The strongest signals are observed in BRCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for CLPS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CLPS shows lower tumor expression in READ and STAD and higher tumor expression in BRCA, UCEC, LUSC and LUAD. The BRCA box plot shows higher CLPS RNA expression in tumor versus normal tissue (log2 FC = +0.766, t-test p < 0.001).
This table shows molecular features associated with CLPS in patient tissues and cancer cell lines. In patient samples, CLPS shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, CLPS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LUNG_SCLC.
