Q-omics provides the consensus-scored ASCL2 profile across patient tissues and cancer cell-line models. ASCL2 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, ASCL2 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, ASCL2 RNA expression shows 14,341 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SKCM, HNSC, and GBM as cancer lineages where ASCL2 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 ASCL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ASCL2 survival associations across molecular data types. ASCL2 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ASCL2 RNA expression–survival associations across cancer types. High ASCL2 expression shows unfavorable associations in BRCA and LGG, but favorable associations in SKCM, BLCA, STAD and PAAD. The SKCM 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 SKCM as the clearest survival context for ASCL2 RNA expression.
This table summarizes ASCL2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for ASCL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ASCL2 shows lower tumor expression in KICH and THCA and higher tumor expression in HNSC, COAD, READ and STAD. The HNSC box plot shows higher ASCL2 RNA expression in tumor versus normal tissue (log2 FC = +1.373, t-test p < 0.001).
This table shows molecular features associated with ASCL2 in patient tissues and cancer cell lines. In patient samples, ASCL2 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, ASCL2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BREAST.
