Q-omics provides the consensus-scored LCORL profile across patient tissues and cancer cell-line models. LCORL expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, LCORL is differentially expressed in 9, with the highest sampling consensus in STAD. Additionally, LCORL RNA expression shows 21,066 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight HNSC, STAD, and UVM as cancer lineages where LCORL 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 LCORL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes LCORL survival associations across molecular data types. LCORL RNA expression shows survival associations in the most cancer types (27), followed by mutation status (5) 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 LCORL RNA expression–survival associations across cancer types. High LCORL expression shows unfavorable associations in UVM and KICH, but favorable associations in HNSC, KIRC, UCEC and READ. The HNSC 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 HNSC as the clearest survival context for LCORL RNA expression.
This table summarizes LCORL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 4. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for LCORL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. LCORL shows lower tumor expression in THCA and higher tumor expression in STAD, LIHC, HNSC, BRCA and CHOL. The STAD box plot shows higher LCORL RNA expression in tumor versus normal tissue (log2 FC = +0.722, t-test p < 0.001).
This table shows molecular features associated with LCORL in patient tissues and cancer cell lines. In patient samples, LCORL shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, LCORL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
