Q-omics provides the consensus-scored GAPT profile across patient tissues and cancer cell-line models. GAPT expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, GAPT is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, GAPT RNA expression shows 22,020 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, KIRC, and LSCC as cancer lineages where GAPT 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 GAPT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GAPT survival associations across molecular data types. GAPT RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GAPT RNA expression–survival associations across cancer types. High GAPT expression shows unfavorable associations in LGG, but favorable associations in HNSC, SKCM, ACC, MESO and LUAD. The HNSC 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 HNSC as the clearest survival context for GAPT RNA expression.
This table summarizes GAPT tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for GAPT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GAPT shows lower tumor expression in LUAD, COAD, LUSC and BLCA and higher tumor expression in KIRC and KIRP. The KIRC box plot shows higher GAPT RNA expression in tumor versus normal tissue (log2 FC = +2.024, t-test p < 0.001).
This table shows molecular features associated with GAPT in patient tissues and cancer cell lines. In patient samples, GAPT 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, GAPT 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_SCLC and BLOOD_Leukemia.
