Q-omics provides the consensus-scored ASPA profile across patient tissues and cancer cell-line models. ASPA expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ASPA is differentially expressed in 17, with the highest sampling consensus in COAD. Additionally, ASPA protein abundance shows 33,906 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, COAD, and LSCC as cancer lineages where ASPA 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 ASPA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ASPA survival associations across molecular data types. ASPA RNA expression shows survival associations in the most cancer types (27), followed by mutation status (1) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ASPA RNA expression–survival associations across cancer types. High ASPA expression shows unfavorable associations in ACC and UCEC, but favorable associations in KIRC, SARC, KIRP and HNSC. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify ACC as the clearest survival context for ASPA RNA expression.
This table summarizes ASPA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 10. The strongest signals are observed in COAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ASPA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ASPA shows lower tumor expression in COAD, KICH, BLCA, LUAD, HNSC and THCA. The COAD box plot shows higher ASPA RNA expression in normal versus tumor tissue (log2 FC = −1.702, t-test p < 0.001).
This table shows molecular features associated with ASPA in patient tissues and cancer cell lines. In patient samples, ASPA shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ASPA 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 BONE and BREAST.
