Q-omics provides the consensus-scored CLPP profile across patient tissues and cancer cell-line models. CLPP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, CLPP is differentially expressed in 12, with the highest sampling consensus in COAD. Additionally, CLPP protein abundance shows 30,007 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SCLC, COAD, and GBM as cancer lineages where CLPP 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 CLPP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CLPP survival associations across molecular data types. CLPP RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) and mass-spec protein abundance (12). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CLPP RNA expression–survival associations across cancer types. High CLPP expression shows unfavorable associations in UVM, ACC, UCS and KICH, but favorable associations in SCLC and KIRC. The SCLC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify SCLC as the clearest survival context for CLPP RNA expression.
This table summarizes CLPP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 11. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CLPP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CLPP shows higher tumor expression in COAD, KIRC, HNSC, BLCA, LIHC and STAD. The COAD box plot shows higher CLPP RNA expression in tumor versus normal tissue (log2 FC = +1.277, t-test p < 0.001).
This table shows molecular features associated with CLPP in patient tissues and cancer cell lines. In patient samples, CLPP 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, CLPP 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 BLOOD_Myeloma and SOFT_TISSUE.
