Q-omics provides the consensus-scored GLMP profile across patient tissues and cancer cell-line models. GLMP expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, GLMP is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, GLMP RNA expression shows 18,315 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, HNSC, and ACC as cancer lineages where GLMP 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 GLMP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GLMP survival associations across molecular data types. GLMP RNA expression shows survival associations in the most cancer types (20), followed by mutation status (2) 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 GLMP RNA expression–survival associations across cancer types. High GLMP expression shows unfavorable associations in LIHC, BLCA, LGG, COAD and UCEC, but favorable associations in BRCA. The LIHC 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 LIHC as the clearest survival context for GLMP RNA expression.
This table summarizes GLMP 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 HNSC for protein.
This table ranks reproducible tumor–normal expression differences for GLMP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GLMP shows higher tumor expression in HNSC, KIRC, BLCA, LIHC, LUAD and THCA. The HNSC box plot shows higher GLMP RNA expression in tumor versus normal tissue (log2 FC = +1.372, t-test p < 0.001).
This table shows molecular features associated with GLMP in patient tissues and cancer cell lines. In patient samples, GLMP 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, GLMP 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 PANCREAS and SOFT_TISSUE.
