Q-omics provides the consensus-scored SERPINA10 profile across patient tissues and cancer cell-line models. SERPINA10 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in OV. Among the 18 cancer types available for tumor–normal comparison, SERPINA10 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, SERPINA10 protein abundance shows 24,290 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight OV, HNSC, and LUAD as cancer lineages where SERPINA10 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 SERPINA10 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERPINA10 survival associations across molecular data types. SERPINA10 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (7) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERPINA10 RNA expression–survival associations across cancer types. High SERPINA10 expression shows unfavorable associations in ACC and KIRP, but favorable associations in OV, LIHC, SKCM and SCLC. The OV 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 OV as the clearest survival context for SERPINA10 RNA expression.
This table summarizes SERPINA10 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SERPINA10. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERPINA10 shows lower tumor expression in LUAD, LIHC, CHOL and KIRP and higher tumor expression in HNSC and COAD. The HNSC box plot shows higher SERPINA10 RNA expression in tumor versus normal tissue (log2 FC = +0.101, t-test p < 0.001).
This table shows molecular features associated with SERPINA10 in patient tissues and cancer cell lines. In patient samples, SERPINA10 shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, SERPINA10 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and LIVER.
