Q-omics provides the consensus-scored ABRAXAS2 profile across patient tissues and cancer cell-line models. ABRAXAS2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ABRAXAS2 is differentially expressed in 12, with the highest sampling consensus in THCA. Additionally, ABRAXAS2 RNA expression shows 20,156 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, THCA, and ACC as cancer lineages where ABRAXAS2 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 ABRAXAS2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ABRAXAS2 survival associations across molecular data types. ABRAXAS2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ABRAXAS2 RNA expression–survival associations across cancer types. High ABRAXAS2 expression shows unfavorable associations in ACC and LIHC, but favorable associations in KIRC, BRCA, LGG and LUAD. 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 ABRAXAS2 RNA expression.
This table summarizes ABRAXAS2 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 6. The strongest signals are observed in THCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for ABRAXAS2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ABRAXAS2 shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, LIHC, STAD and KIRP. The THCA box plot shows higher ABRAXAS2 RNA expression in normal versus tumor tissue (log2 FC = −0.561, t-test p < 0.001).
This table shows molecular features associated with ABRAXAS2 in patient tissues and cancer cell lines. In patient samples, ABRAXAS2 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, ABRAXAS2 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 PANCREAS and BLOOD_Lymphoma.
