Q-omics provides the consensus-scored SFN profile across patient tissues and cancer cell-line models. SFN expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SFN is differentially expressed in 15, with the highest sampling consensus in THCA. Additionally, SFN protein abundance shows 22,164 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight ACC, THCA, and HNSC as cancer lineages where SFN 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 SFN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SFN survival associations across molecular data types. SFN RNA expression shows survival associations in the most cancer types (27), 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 SFN RNA expression–survival associations across cancer types. High SFN expression shows unfavorable associations in ACC, LIHC, KIRC, SKCM and LUAD, but favorable associations in UCEC. The ACC 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 ACC as the clearest survival context for SFN RNA expression.
This table summarizes SFN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SFN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SFN shows lower tumor expression in KICH and higher tumor expression in THCA, UCEC, LIHC, LUSC and LUAD. The THCA box plot shows higher SFN RNA expression in tumor versus normal tissue (log2 FC = +4.548, t-test p < 0.001).
This table shows molecular features associated with SFN in patient tissues and cancer cell lines. In patient samples, SFN shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, SFN 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 BLOOD_Lymphoma and SOFT_TISSUE.
