Q-omics provides the consensus-scored AGBL2 profile across patient tissues and cancer cell-line models. AGBL2 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, AGBL2 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, AGBL2 RNA expression shows 19,461 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, and UVM as cancer lineages where AGBL2 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 AGBL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGBL2 survival associations across molecular data types. AGBL2 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AGBL2 RNA expression–survival associations across cancer types. High AGBL2 expression shows unfavorable associations in KIRC and LGG, but favorable associations in BRCA, BLCA, STAD and UCS. The KIRC 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 KIRC as the clearest survival context for AGBL2 RNA expression.
This table summarizes AGBL2 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 KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for AGBL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGBL2 shows lower tumor expression in KICH and higher tumor expression in KIRC, KIRP, HNSC, STAD and BRCA. The KIRC box plot shows higher AGBL2 RNA expression in tumor versus normal tissue (log2 FC = +0.429, t-test p < 0.001).
This table shows molecular features associated with AGBL2 in patient tissues and cancer cell lines. In patient samples, AGBL2 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, AGBL2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
