pro-melanin concentrating hormone like 1 (pseudogene)Genealiases: []
Q-omics provides the consensus-scored PMCHL1 profile across patient tissues and cancer cell-line models. PMCHL1 expression is associated with patient survival in 7 of 34 cancer types, with the highest sampling consensus in KIRP. Additionally, PMCHL1 RNA expression shows 6,546 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, and GBM as cancer lineages where PMCHL1 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 PMCHL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PMCHL1 survival associations across molecular data types. PMCHL1 RNA expression shows survival associations in the most cancer types (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PMCHL1 RNA expression–survival associations across cancer types. High PMCHL1 expression shows unfavorable associations in KIRP, LIHC, STAD and KIRC, but favorable associations in ESCA and HNSC. The KIRP Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify KIRP as the clearest survival context for PMCHL1 RNA expression.
This table shows molecular features associated with PMCHL1 in patient tissues and cancer cell lines. In patient samples, PMCHL1 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, PMCHL1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in NCI60_ALL.
