Q-omics provides the consensus-scored ASCL3 profile across patient tissues and cancer cell-line models. ASCL3 expression is associated with patient survival in 15 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, ASCL3 is differentially expressed in 5, with the highest sampling consensus in KIRC. Additionally, ASCL3 RNA expression shows 11,487 significant gene co-expression associations, with the highest sampling consensus in LAML. Together, these results highlight LIHC, KIRC, and LAML as cancer lineages where ASCL3 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 ASCL3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ASCL3 survival associations across molecular data types. ASCL3 RNA expression shows survival associations in the most cancer types (15), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ASCL3 RNA expression–survival associations across cancer types. High ASCL3 expression shows unfavorable associations in LIHC, ACC, HNSC, LUSC and SKCM, but favorable associations in SCLC. 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 ASCL3 RNA expression.
This table summarizes ASCL3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for ASCL3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ASCL3 shows lower tumor expression in KIRC, HNSC, KIRP and KICH and higher tumor expression in ESCA. The KIRC box plot shows higher ASCL3 RNA expression in normal versus tumor tissue (log2 FC = −0.351, t-test p < 0.001).
This table shows molecular features associated with ASCL3 in patient tissues and cancer cell lines. In patient samples, ASCL3 shows the broadest associations at the RNA and protein expression levels, with LAML recurring as the lineage with the largest associated feature set. In cancer cell lines, ASCL3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BLOOD_Leukemia.
