Q-omics provides the consensus-scored XG profile across patient tissues and cancer cell-line models. XG expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, XG is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, XG protein abundance shows 24,612 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight STAD, KICH, and LSCC as cancer lineages where XG 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 XG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes XG survival associations across molecular data types. XG RNA expression shows survival associations in the most cancer types (22), followed by mutation status (2) 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 XG RNA expression–survival associations across cancer types. High XG expression shows unfavorable associations in STAD, ACC, LGG, BLCA and BRCA, but favorable associations in ESCA. The STAD 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 STAD as the clearest survival context for XG RNA expression.
This table summarizes XG tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 8. The strongest signals are observed in KICH for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for XG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. XG shows lower tumor expression in KICH, THCA, KIRP, BRCA and KIRC and higher tumor expression in LUSC. The KICH box plot shows higher XG RNA expression in normal versus tumor tissue (log2 FC = −1.040, t-test p < 0.001).
This table shows molecular features associated with XG in patient tissues and cancer cell lines. In patient samples, XG shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, XG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in STOMACH and BONE.
