Q-omics provides the consensus-scored GCNT3 profile across patient tissues and cancer cell-line models. GCNT3 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, GCNT3 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, GCNT3 RNA expression shows 13,443 significant gene co-expression associations, with the highest sampling consensus in ESCA. Together, these results highlight UCEC, HNSC, and ESCA as cancer lineages where GCNT3 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 GCNT3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GCNT3 survival associations across molecular data types. GCNT3 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GCNT3 RNA expression–survival associations across cancer types. High GCNT3 expression shows unfavorable associations in KIRP, LUAD, LGG, LIHC and UVM, but favorable associations in UCEC. The UCEC 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 UCEC as the clearest survival context for GCNT3 RNA expression.
This table summarizes GCNT3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for GCNT3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GCNT3 shows lower tumor expression in HNSC, COAD and KICH and higher tumor expression in KIRP, LUAD and LIHC. The HNSC box plot shows higher GCNT3 RNA expression in normal versus tumor tissue (log2 FC = −2.376, t-test p < 0.001).
This table shows molecular features associated with GCNT3 in patient tissues and cancer cell lines. In patient samples, GCNT3 shows the broadest associations at the RNA and protein expression levels, with ESCA recurring as the lineage with the largest associated feature set. In cancer cell lines, GCNT3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and OVARY.
