Q-omics provides the consensus-scored B4GALT3 profile across patient tissues and cancer cell-line models. B4GALT3 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, B4GALT3 is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, B4GALT3 RNA expression shows 18,359 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, HNSC, and ACC as cancer lineages where B4GALT3 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 B4GALT3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes B4GALT3 survival associations across molecular data types. B4GALT3 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (8) 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 B4GALT3 RNA expression–survival associations across cancer types. High B4GALT3 expression shows unfavorable associations in LIHC, ACC, KIRP, CESC, BRCA and KIRC. 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 B4GALT3 RNA expression.
This table summarizes B4GALT3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for B4GALT3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. B4GALT3 shows higher tumor expression in HNSC, BLCA, LUAD, COAD, KIRC and LIHC. The HNSC box plot shows higher B4GALT3 RNA expression in tumor versus normal tissue (log2 FC = +1.070, t-test p < 0.001).
This table shows molecular features associated with B4GALT3 in patient tissues and cancer cell lines. In patient samples, B4GALT3 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, B4GALT3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BONE.
