Q-omics provides the consensus-scored SERPINF2 profile across patient tissues and cancer cell-line models. SERPINF2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, SERPINF2 is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, SERPINF2 protein abundance shows 27,125 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight HNSC, KIRC, and GBM as cancer lineages where SERPINF2 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 SERPINF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERPINF2 survival associations across molecular data types. SERPINF2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERPINF2 RNA expression–survival associations across cancer types. High SERPINF2 expression shows unfavorable associations in ACC, but favorable associations in HNSC, LIHC, KIRC, UCEC and CESC. The HNSC 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 HNSC as the clearest survival context for SERPINF2 RNA expression.
This table summarizes SERPINF2 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 6. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SERPINF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERPINF2 shows lower tumor expression in KICH, KIRP, LIHC and LUSC and higher tumor expression in KIRC and COAD. The KIRC box plot shows higher SERPINF2 RNA expression in tumor versus normal tissue (log2 FC = +2.160, t-test p < 0.001).
This table shows molecular features associated with SERPINF2 in patient tissues and cancer cell lines. In patient samples, SERPINF2 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, SERPINF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in SKIN and LIVER.
