Q-omics provides the consensus-scored PCSK1 profile across patient tissues and cancer cell-line models. PCSK1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, PCSK1 is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, PCSK1 RNA expression shows 16,100 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight HNSC, and UVM as cancer lineages where PCSK1 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 PCSK1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PCSK1 survival associations across molecular data types. PCSK1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PCSK1 RNA expression–survival associations across cancer types. High PCSK1 expression shows unfavorable associations in UVM, UCEC, KIRP and LUAD, but favorable associations in HNSC and SKCM. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify HNSC as the clearest survival context for PCSK1 RNA expression.
This table summarizes PCSK1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 2. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for PCSK1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PCSK1 shows lower tumor expression in KIRP and THCA and higher tumor expression in HNSC, LUSC, LUAD and BRCA. The HNSC box plot shows higher PCSK1 RNA expression in tumor versus normal tissue (log2 FC = +1.285, t-test p < 0.001).
This table shows molecular features associated with PCSK1 in patient tissues and cancer cell lines. In patient samples, PCSK1 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, PCSK1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and CNS.
