Q-omics provides the consensus-scored HLCS profile across patient tissues and cancer cell-line models. HLCS expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, HLCS is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, HLCS RNA expression shows 20,364 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, COAD, and ACC as cancer lineages where HLCS 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 HLCS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HLCS survival associations across molecular data types. HLCS RNA expression shows survival associations in the most cancer types (21), followed by mutation status (2) 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 HLCS RNA expression–survival associations across cancer types. High HLCS expression shows unfavorable associations in UVM, LGG, MESO, ACC, STAD and KICH. 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 HLCS RNA expression.
This table summarizes HLCS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in COAD for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for HLCS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HLCS shows lower tumor expression in THCA and KICH and higher tumor expression in COAD, BLCA, LUAD and UCEC. The COAD box plot shows higher HLCS RNA expression in tumor versus normal tissue (log2 FC = +0.869, t-test p < 0.001).
This table shows molecular features associated with HLCS in patient tissues and cancer cell lines. In patient samples, HLCS 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, HLCS 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 BLOOD_Leukemia and LARGE_INTESTINE.
