Q-omics provides the consensus-scored ALPG profile across patient tissues and cancer cell-line models. ALPG expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ALPG is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, ALPG RNA expression shows 9,480 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight MESO, HNSC, and TGCT as cancer lineages where ALPG 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 ALPG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ALPG survival associations across molecular data types. ALPG RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ALPG RNA expression–survival associations across cancer types. High ALPG expression shows unfavorable associations in MESO, LIHC, BLCA, KIRP and COAD, but favorable associations in UCEC. The MESO 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 MESO as the clearest survival context for ALPG RNA expression.
This table summarizes ALPG tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for ALPG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ALPG shows lower tumor expression in LUSC and COAD and higher tumor expression in HNSC, STAD, BLCA and BRCA. The HNSC box plot shows higher ALPG RNA expression in tumor versus normal tissue (log2 FC = +0.317, t-test p = .001).
This table shows molecular features associated with ALPG in patient tissues and cancer cell lines. In patient samples, ALPG 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, ALPG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in LIVER and PANCREAS.
