Q-omics provides the consensus-scored XYLB profile across patient tissues and cancer cell-line models. XYLB expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, XYLB is differentially expressed in 12, with the highest sampling consensus in BLCA. Additionally, XYLB RNA expression shows 18,941 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, BLCA, and TGCT as cancer lineages where XYLB 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 XYLB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes XYLB survival associations across molecular data types. XYLB RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible XYLB RNA expression–survival associations across cancer types. High XYLB expression shows unfavorable associations in KICH, MESO, LGG and SARC, but favorable associations in KIRC and READ. The KIRC 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 KIRC as the clearest survival context for XYLB RNA expression.
This table summarizes XYLB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in BLCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for XYLB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. XYLB shows lower tumor expression in KICH and higher tumor expression in BLCA, LUAD, LUSC, STAD and UCEC. The BLCA box plot shows higher XYLB RNA expression in tumor versus normal tissue (log2 FC = +0.938, t-test p < 0.001).
This table shows molecular features associated with XYLB in patient tissues and cancer cell lines. In patient samples, XYLB 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, XYLB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BREAST and SOFT_TISSUE.
