Q-omics provides the consensus-scored SETSIP profile across patient tissues and cancer cell-line models. SETSIP expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, SETSIP is differentially expressed in 12, with the highest sampling consensus in COAD. Additionally, SETSIP RNA expression shows 16,480 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, COAD, and ACC as cancer lineages where SETSIP 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 SETSIP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SETSIP survival associations across molecular data types. SETSIP RNA expression shows survival associations in the most cancer types (20), followed by 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 SETSIP RNA expression–survival associations across cancer types. High SETSIP expression shows unfavorable associations in LIHC, KIRP, SKCM and PAAD, but favorable associations in CESC and KIRC. The LIHC 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 LIHC as the clearest survival context for SETSIP RNA expression.
This table summarizes SETSIP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRP for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for SETSIP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SETSIP shows higher tumor expression in COAD, KIRP, HNSC, STAD, LUSC and BRCA. The COAD box plot shows higher SETSIP RNA expression in tumor versus normal tissue (log2 FC = +0.776, t-test p < 0.001).
This table shows molecular features associated with SETSIP in patient tissues and cancer cell lines. In patient samples, SETSIP shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, SETSIP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and OVARY.
