Q-omics provides the consensus-scored OPLAH profile across patient tissues and cancer cell-line models. OPLAH expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, OPLAH is differentially expressed in 13, with the highest sampling consensus in THCA. Additionally, OPLAH RNA expression shows 17,742 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, THCA, and ACC as cancer lineages where OPLAH 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 OPLAH — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OPLAH survival associations across molecular data types. OPLAH RNA expression shows survival associations in the most cancer types (28), followed by mutation status (8) 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 OPLAH RNA expression–survival associations across cancer types. High OPLAH expression shows unfavorable associations in UVM, ESCA, COAD and LGG, but favorable associations in KIRP and STAD. The UVM 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 UVM as the clearest survival context for OPLAH RNA expression.
This table summarizes OPLAH 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 THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for OPLAH. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. OPLAH shows lower tumor expression in THCA and higher tumor expression in STAD, LIHC, BLCA, LUAD and LUSC. The THCA box plot shows higher OPLAH RNA expression in normal versus tumor tissue (log2 FC = −1.622, t-test p < 0.001).
This table shows molecular features associated with OPLAH in patient tissues and cancer cell lines. In patient samples, OPLAH shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, OPLAH 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 LARGE_INTESTINE.
