Q-omics provides the consensus-scored LGALS9B profile across patient tissues and cancer cell-line models. LGALS9B expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in CHOL. Among the 18 cancer types available for tumor–normal comparison, LGALS9B is differentially expressed in 9, with the highest sampling consensus in COAD. Additionally, LGALS9B RNA expression shows 8,086 significant gene co-expression associations, with the highest sampling consensus in BLCA. Together, these results highlight CHOL, COAD, and BLCA as cancer lineages where LGALS9B 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 LGALS9B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes LGALS9B survival associations across molecular data types. LGALS9B RNA expression shows survival associations in the most cancer types (28), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible LGALS9B RNA expression–survival associations across cancer types. High LGALS9B expression shows unfavorable associations in CHOL, LUAD, LGG and LAML, but favorable associations in MESO and SCLC. The CHOL 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 CHOL as the clearest survival context for LGALS9B RNA expression.
This table summarizes LGALS9B tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for LGALS9B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. LGALS9B shows lower tumor expression in COAD, READ and STAD and higher tumor expression in KIRC, KIRP and BRCA. The COAD box plot shows higher LGALS9B RNA expression in normal versus tumor tissue (log2 FC = −1.866, t-test p < 0.001).
This table shows molecular features associated with LGALS9B in patient tissues and cancer cell lines. In patient samples, LGALS9B shows the broadest associations at the RNA and protein expression levels, with BLCA recurring as the lineage with the largest associated feature set. In cancer cell lines, LGALS9B 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 LUNG_NSCLC_LUAD and SKIN.
