Q-omics provides the consensus-scored PCSK9 profile across patient tissues and cancer cell-line models. PCSK9 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, PCSK9 is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, PCSK9 protein abundance shows 12,414 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRP, COAD, and PDAC as cancer lineages where PCSK9 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 PCSK9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PCSK9 survival associations across molecular data types. PCSK9 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) 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 PCSK9 RNA expression–survival associations across cancer types. High PCSK9 expression shows unfavorable associations in KIRP, KIRC, BLCA, LUAD, SKCM and LIHC. 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 PCSK9 RNA expression.
This table summarizes PCSK9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PCSK9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PCSK9 shows lower tumor expression in KIRC, LUAD and KIRP and higher tumor expression in COAD, HNSC and STAD. The COAD box plot shows higher PCSK9 RNA expression in tumor versus normal tissue (log2 FC = +4.644, t-test p < 0.001).
This table shows molecular features associated with PCSK9 in patient tissues and cancer cell lines. In patient samples, PCSK9 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, PCSK9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BONE.
