Q-omics provides the consensus-scored B4GALT5 profile across patient tissues and cancer cell-line models. B4GALT5 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in PAAD. Among the 18 cancer types available for tumor–normal comparison, B4GALT5 is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, B4GALT5 RNA expression shows 19,928 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight PAAD, KIRP, and UVM as cancer lineages where B4GALT5 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 B4GALT5 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes B4GALT5 survival associations across molecular data types. B4GALT5 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible B4GALT5 RNA expression–survival associations across cancer types. High B4GALT5 expression shows unfavorable associations in PAAD, ACC, LIHC, BLCA, LGG and MESO. The PAAD 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 PAAD as the clearest survival context for B4GALT5 RNA expression.
This table summarizes B4GALT5 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 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for B4GALT5. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. B4GALT5 shows lower tumor expression in KICH and higher tumor expression in KIRP, KIRC, THCA, STAD and CHOL. The KIRP box plot shows higher B4GALT5 RNA expression in tumor versus normal tissue (log2 FC = +1.958, t-test p < 0.001).
This table shows molecular features associated with B4GALT5 in patient tissues and cancer cell lines. In patient samples, B4GALT5 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, B4GALT5 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 BONE and LARGE_INTESTINE.
