ATP binding cassette subfamily G member 5Genealiases: STSL · STSL2
Q-omics provides the consensus-scored ABCG5 profile across patient tissues and cancer cell-line models. ABCG5 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, ABCG5 is differentially expressed in 7, with the highest sampling consensus in KICH. Additionally, ABCG5 RNA expression shows 16,399 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight SCLC, KICH, and KIRP as cancer lineages where ABCG5 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 ABCG5 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ABCG5 survival associations across molecular data types. ABCG5 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ABCG5 RNA expression–survival associations across cancer types. High ABCG5 expression shows unfavorable associations in SKCM, ACC and STAD, but favorable associations in SCLC, MESO and PAAD. The SCLC 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 SCLC as the clearest survival context for ABCG5 RNA expression.
This table summarizes ABCG5 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 1. The strongest signals are observed in KICH for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ABCG5. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ABCG5 shows lower tumor expression in KICH, THCA, KIRC, CHOL and LIHC and higher tumor expression in HNSC. The KICH box plot shows higher ABCG5 RNA expression in normal versus tumor tissue (log2 FC = −0.664, t-test p < 0.001).
This table shows molecular features associated with ABCG5 in patient tissues and cancer cell lines. In patient samples, ABCG5 shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, ABCG5 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
