Q-omics provides the consensus-scored ASPN profile across patient tissues and cancer cell-line models. ASPN expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, ASPN is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, ASPN protein abundance shows 31,743 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRP, HNSC, and PDAC as cancer lineages where ASPN 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 ASPN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ASPN survival associations across molecular data types. ASPN RNA expression shows survival associations in the most cancer types (20), followed by mutation status (5) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ASPN RNA expression–survival associations across cancer types. High ASPN expression shows unfavorable associations in KIRP, SCLC, UVM, LGG and KICH, but favorable associations in UCS. The KIRP 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 KIRP as the clearest survival context for ASPN RNA expression.
This table summarizes ASPN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ASPN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ASPN shows lower tumor expression in KICH, KIRP and UCEC and higher tumor expression in HNSC, BRCA and LUAD. The HNSC box plot shows higher ASPN RNA expression in tumor versus normal tissue (log2 FC = +3.247, t-test p < 0.001).
This table shows molecular features associated with ASPN in patient tissues and cancer cell lines. In patient samples, ASPN 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, ASPN 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 LUNG_NSCLC_LUSC and SOFT_TISSUE.
