Q-omics provides the consensus-scored PFN4 profile across patient tissues and cancer cell-line models. PFN4 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PFN4 is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, PFN4 RNA expression shows 19,338 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, KICH, and UVM as cancer lineages where PFN4 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 PFN4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PFN4 survival associations across molecular data types. PFN4 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PFN4 RNA expression–survival associations across cancer types. High PFN4 expression shows unfavorable associations in KIRC, ACC, UVM, LIHC, LGG and BLCA. The KIRC 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 KIRC as the clearest survival context for PFN4 RNA expression.
This table summarizes PFN4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for PFN4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PFN4 shows lower tumor expression in KICH and higher tumor expression in LIHC, LUAD, BLCA, THCA and HNSC. The KICH box plot shows higher PFN4 RNA expression in normal versus tumor tissue (log2 FC = −1.199, t-test p < 0.001).
This table shows molecular features associated with PFN4 in patient tissues and cancer cell lines. In patient samples, PFN4 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, PFN4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in STOMACH and OVARY.
