Q-omics provides the consensus-scored LHCGR profile across patient tissues and cancer cell-line models. LHCGR expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, LHCGR is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, LHCGR protein abundance shows 17,812 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRP, HNSC, and LUAD as cancer lineages where LHCGR 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 LHCGR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes LHCGR survival associations across molecular data types. LHCGR RNA expression shows survival associations in the most cancer types (22), followed by mutation status (9) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible LHCGR RNA expression–survival associations across cancer types. High LHCGR expression shows unfavorable associations in READ, KIRC and UVM, but favorable associations in KIRP, SKCM and LUAD. The KIRP 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 KIRP as the clearest survival context for LHCGR RNA expression.
This table summarizes LHCGR tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 2. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for LHCGR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. LHCGR shows lower tumor expression in HNSC, THCA, BRCA, LUSC and KICH and higher tumor expression in LUAD. The HNSC box plot shows higher LHCGR RNA expression in normal versus tumor tissue (log2 FC = −0.020, t-test p = .014).
This table shows molecular features associated with LHCGR in patient tissues and cancer cell lines. In patient samples, LHCGR shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, LHCGR RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in OVARY and LARGE_INTESTINE.
