amyloid P component, serumGenealiases: HEL-S-92n · PTX2 · SAP
Q-omics provides the consensus-scored APCS profile across patient tissues and cancer cell-line models. APCS expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, APCS is differentially expressed in 5, with the highest sampling consensus in LIHC. Additionally, APCS protein abundance shows 25,222 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRP, LIHC, and LSCC as cancer lineages where APCS 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 APCS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APCS survival associations across molecular data types. APCS RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APCS RNA expression–survival associations across cancer types. High APCS expression shows unfavorable associations in KIRP, STAD, BRCA, BLCA and GBM, but favorable associations in ACC. 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 APCS RNA expression.
This table summarizes APCS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5, while mass-spec protein shows differences in 7. The strongest signals are observed in LIHC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for APCS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APCS shows lower tumor expression in LIHC, KICH, UCEC and CHOL and higher tumor expression in LUAD. The LIHC box plot shows higher APCS RNA expression in normal versus tumor tissue (log2 FC = −2.493, t-test p < 0.001).
This table shows molecular features associated with APCS in patient tissues and cancer cell lines. In patient samples, APCS 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, APCS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and OESOPHAGUS.
