Q-omics provides the consensus-scored GLB1L profile across patient tissues and cancer cell-line models. GLB1L expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, GLB1L is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, GLB1L RNA expression shows 19,059 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, and ACC as cancer lineages where GLB1L 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 GLB1L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GLB1L survival associations across molecular data types. GLB1L RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GLB1L RNA expression–survival associations across cancer types. High GLB1L expression shows unfavorable associations in LGG, ACC and LIHC, but favorable associations in KIRC, LUAD and MESO. The KIRC 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 KIRC as the clearest survival context for GLB1L RNA expression.
This table summarizes GLB1L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GLB1L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GLB1L shows lower tumor expression in KICH and LUSC and higher tumor expression in KIRC, LIHC, COAD and STAD. The KIRC box plot shows higher GLB1L RNA expression in tumor versus normal tissue (log2 FC = +2.003, t-test p < 0.001).
This table shows molecular features associated with GLB1L in patient tissues and cancer cell lines. In patient samples, GLB1L 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, GLB1L 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_Myeloma and UPPER_AERODIGESTIVE_TRACT.
