ATP binding cassette subfamily A member 11, pseudogeneGenealiases: ABCA11 · EST1133530
Q-omics provides the consensus-scored ABCA11P profile across patient tissues and cancer cell-line models. ABCA11P expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, ABCA11P is differentially expressed in 7, with the highest sampling consensus in KIRC. Additionally, ABCA11P RNA expression shows 19,156 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight LIHC, KIRC, and UVM as cancer lineages where ABCA11P 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 ABCA11P — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ABCA11P survival associations across molecular data types. ABCA11P RNA expression shows survival associations in the most cancer types (21). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ABCA11P RNA expression–survival associations across cancer types. High ABCA11P expression shows unfavorable associations in LIHC, KIRC and KIRP, but favorable associations in LUAD, CHOL and READ. The LIHC 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 LIHC as the clearest survival context for ABCA11P RNA expression.
This table summarizes ABCA11P tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for ABCA11P. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ABCA11P shows lower tumor expression in KIRC, KICH and THCA and higher tumor expression in HNSC, LIHC and STAD. The KIRC box plot shows higher ABCA11P RNA expression in normal versus tumor tissue (log2 FC = −0.350, t-test p < 0.001).
This table shows molecular features associated with ABCA11P in patient tissues and cancer cell lines. In patient samples, ABCA11P 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, ABCA11P 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 BLOOD_Myeloma and NCI60_ALL.
