Q-omics provides the consensus-scored GALM profile across patient tissues and cancer cell-line models. GALM expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, GALM is differentially expressed in 12, with the highest sampling consensus in COAD. Additionally, GALM protein abundance shows 23,490 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, COAD, and LSCC as cancer lineages where GALM 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 GALM — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GALM survival associations across molecular data types. GALM RNA expression shows survival associations in the most cancer types (25), followed by mutation status (2) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GALM RNA expression–survival associations across cancer types. High GALM expression shows unfavorable associations in LIHC, LGG and KICH, but favorable associations in KIRC, HNSC and UVM. 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 GALM RNA expression.
This table summarizes GALM tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GALM. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GALM shows lower tumor expression in COAD, KIRP, KICH, THCA and KIRC and higher tumor expression in STAD. The COAD box plot shows higher GALM RNA expression in normal versus tumor tissue (log2 FC = −1.138, t-test p < 0.001).
This table shows molecular features associated with GALM in patient tissues and cancer cell lines. In patient samples, GALM shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, GALM 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 BREAST and BLOOD_Lymphoma.
