Q-omics provides the consensus-scored CHERP profile across patient tissues and cancer cell-line models. CHERP expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, CHERP is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, CHERP protein abundance shows 33,760 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SCLC, HNSC, and GBM as cancer lineages where CHERP 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 CHERP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CHERP survival associations across molecular data types. CHERP RNA expression shows survival associations in the most cancer types (25), followed by mutation status (8) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CHERP RNA expression–survival associations across cancer types. High CHERP expression shows unfavorable associations in KICH, KIRP, ACC and SKCM, but favorable associations in SCLC and UCS. 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 CHERP RNA expression.
This table summarizes CHERP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for CHERP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CHERP shows higher tumor expression in HNSC, COAD, STAD, LIHC, KIRP and LUSC. The HNSC box plot shows higher CHERP RNA expression in tumor versus normal tissue (log2 FC = +0.860, t-test p < 0.001).
This table shows molecular features associated with CHERP in patient tissues and cancer cell lines. In patient samples, CHERP 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, CHERP 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 URINARY_TRACT and BLOOD_Lymphoma.
