Q-omics provides the consensus-scored B4GALNT3 profile across patient tissues and cancer cell-line models. B4GALNT3 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, B4GALNT3 is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, B4GALNT3 RNA expression shows 17,683 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UVM, KIRC, and TGCT as cancer lineages where B4GALNT3 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 B4GALNT3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes B4GALNT3 survival associations across molecular data types. B4GALNT3 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (8) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible B4GALNT3 RNA expression–survival associations across cancer types. High B4GALNT3 expression shows unfavorable associations in UVM, MESO, ACC and KIRP, but favorable associations in UCEC and SCLC. The UVM 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 UVM as the clearest survival context for B4GALNT3 RNA expression.
This table summarizes B4GALNT3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for B4GALNT3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. B4GALNT3 shows lower tumor expression in KIRC, COAD and KIRP and higher tumor expression in THCA, HNSC and UCEC. The KIRC box plot shows higher B4GALNT3 RNA expression in normal versus tumor tissue (log2 FC = −2.586, t-test p < 0.001).
This table shows molecular features associated with B4GALNT3 in patient tissues and cancer cell lines. In patient samples, B4GALNT3 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, B4GALNT3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and SOFT_TISSUE.
