ATP binding cassette subfamily G member 1Genealiases: ABC8 · WHITE1
Q-omics provides the consensus-scored ABCG1 profile across patient tissues and cancer cell-line models. ABCG1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ABCG1 is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, ABCG1 protein abundance shows 16,643 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRC, and PDAC as cancer lineages where ABCG1 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 ABCG1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ABCG1 survival associations across molecular data types. ABCG1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) 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 ABCG1 RNA expression–survival associations across cancer types. High ABCG1 expression shows favorable associations in KIRC, SKCM, SCLC, UCEC, HNSC and LGG. 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 ABCG1 RNA expression.
This table summarizes ABCG1 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 KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ABCG1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ABCG1 shows lower tumor expression in LUAD and higher tumor expression in KIRC, HNSC, KICH, KIRP and LIHC. The KIRC box plot shows higher ABCG1 RNA expression in tumor versus normal tissue (log2 FC = +1.510, t-test p < 0.001).
This table shows molecular features associated with ABCG1 in patient tissues and cancer cell lines. In patient samples, ABCG1 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, ABCG1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BREAST.
