Q-omics provides the consensus-scored ALG13 profile across patient tissues and cancer cell-line models. ALG13 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ALG13 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, ALG13 RNA expression shows 20,431 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UVM, and HNSC as cancer lineages where ALG13 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 ALG13 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ALG13 survival associations across molecular data types. ALG13 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) 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 ALG13 RNA expression–survival associations across cancer types. High ALG13 expression shows unfavorable associations in UVM, COAD, KIRP and KICH, but favorable associations in SKCM and OV. The UVM 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 UVM as the clearest survival context for ALG13 RNA expression.
This table summarizes ALG13 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ALG13. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ALG13 shows lower tumor expression in THCA and higher tumor expression in HNSC, LIHC, KIRC, STAD and COAD. The HNSC box plot shows higher ALG13 RNA expression in tumor versus normal tissue (log2 FC = +0.486, t-test p < 0.001).
This table shows molecular features associated with ALG13 in patient tissues and cancer cell lines. In patient samples, ALG13 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, ALG13 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 BLOOD_Leukemia and LARGE_INTESTINE.
