ATP binding cassette subfamily G member 8Genealiases: GBD4 · STSL · STSL1
Q-omics provides the consensus-scored ABCG8 profile across patient tissues and cancer cell-line models. ABCG8 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in CHOL. Among the 18 cancer types available for tumor–normal comparison, ABCG8 is differentially expressed in 12, with the highest sampling consensus in LIHC. Additionally, ABCG8 RNA expression shows 12,621 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight CHOL, LIHC, and TGCT as cancer lineages where ABCG8 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 ABCG8 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ABCG8 survival associations across molecular data types. ABCG8 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ABCG8 RNA expression–survival associations across cancer types. High ABCG8 expression shows unfavorable associations in CHOL, STAD and KIRC, but favorable associations in MESO, BLCA and SCLC. The CHOL Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify CHOL as the clearest survival context for ABCG8 RNA expression.
This table summarizes ABCG8 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for ABCG8. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ABCG8 shows lower tumor expression in LIHC, KIRC, CHOL, BRCA and KICH and higher tumor expression in LUSC. The LIHC box plot shows higher ABCG8 RNA expression in normal versus tumor tissue (log2 FC = −1.319, t-test p = .002).
This table shows molecular features associated with ABCG8 in patient tissues and cancer cell lines. In patient samples, ABCG8 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, ABCG8 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and PANCREAS.
