Q-omics provides the consensus-scored A4GNT profile across patient tissues and cancer cell-line models. A4GNT expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, A4GNT is differentially expressed in 10, with the highest sampling consensus in LUSC. Additionally, A4GNT RNA expression shows 15,605 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight MESO, LUSC, and KIRP as cancer lineages where A4GNT 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 A4GNT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes A4GNT survival associations across molecular data types. A4GNT RNA expression shows survival associations in the most cancer types (19), followed by mutation status (3) 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 A4GNT RNA expression–survival associations across cancer types. High A4GNT expression shows unfavorable associations in MESO and LUAD, but favorable associations in KIRP, ESCA, HNSC and BLCA. The MESO Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .004). Together, the overview and detailed table identify MESO as the clearest survival context for A4GNT RNA expression.
This table summarizes A4GNT tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 5. The strongest signals are observed in LUSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for A4GNT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. A4GNT shows lower tumor expression in LUSC, KICH, BRCA and LUAD and higher tumor expression in KIRC and CHOL. The LUSC box plot shows higher A4GNT RNA expression in normal versus tumor tissue (log2 FC = −0.291, t-test p < 0.001).
This table shows molecular features associated with A4GNT in patient tissues and cancer cell lines. In patient samples, A4GNT shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, A4GNT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LUNG_SCLC.
