Q-omics provides the consensus-scored LLGL1 profile across patient tissues and cancer cell-line models. LLGL1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, LLGL1 is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, LLGL1 protein abundance shows 28,814 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UCS, HNSC, and GBM as cancer lineages where LLGL1 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 LLGL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes LLGL1 survival associations across molecular data types. LLGL1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (6) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible LLGL1 RNA expression–survival associations across cancer types. High LLGL1 expression shows unfavorable associations in MESO and LIHC, but favorable associations in UCS, SCLC, READ and KIRC. The UCS 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 UCS as the clearest survival context for LLGL1 RNA expression.
This table summarizes LLGL1 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 8. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for LLGL1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. LLGL1 shows lower tumor expression in KICH and higher tumor expression in HNSC, COAD, LIHC, THCA and LUAD. The HNSC box plot shows higher LLGL1 RNA expression in tumor versus normal tissue (log2 FC = +0.893, t-test p < 0.001).
This table shows molecular features associated with LLGL1 in patient tissues and cancer cell lines. In patient samples, LLGL1 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, LLGL1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and BLOOD_Leukemia.
