Q-omics provides the consensus-scored OPCML profile across patient tissues and cancer cell-line models. OPCML expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, OPCML is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, OPCML RNA expression shows 16,539 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, KIRP, and GBM as cancer lineages where OPCML 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 OPCML — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OPCML survival associations across molecular data types. OPCML RNA expression shows survival associations in the most cancer types (19), followed by mutation status (7) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OPCML RNA expression–survival associations across cancer types. High OPCML expression shows unfavorable associations in LUSC, STAD and OV, but favorable associations in KIRC, UCEC and LGG. The KIRC 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 KIRC as the clearest survival context for OPCML RNA expression.
This table summarizes OPCML tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11. The strongest signals are observed in KIRP for RNA.
This table ranks reproducible tumor–normal expression differences for OPCML. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. OPCML shows lower tumor expression in KIRP, KICH, BLCA and STAD and higher tumor expression in LUAD and BRCA. The KIRP box plot shows higher OPCML RNA expression in normal versus tumor tissue (log2 FC = −1.488, t-test p < 0.001).
This table shows molecular features associated with OPCML in patient tissues and cancer cell lines. In patient samples, OPCML 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, OPCML RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
