Q-omics provides the consensus-scored SEPHS1 profile across patient tissues and cancer cell-line models. SEPHS1 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SEPHS1 is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, SEPHS1 RNA expression shows 20,293 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and COAD as cancer lineages where SEPHS1 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 SEPHS1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SEPHS1 survival associations across molecular data types. SEPHS1 RNA expression shows survival associations in the most cancer types (28), 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 SEPHS1 RNA expression–survival associations across cancer types. High SEPHS1 expression shows unfavorable associations in ACC, LIHC and COAD, but favorable associations in KIRC, LUSC and LGG. 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 SEPHS1 RNA expression.
This table summarizes SEPHS1 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 6. The strongest signals are observed in COAD for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SEPHS1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SEPHS1 shows lower tumor expression in THCA and KICH and higher tumor expression in COAD, LIHC, STAD and BLCA. The COAD box plot shows higher SEPHS1 RNA expression in tumor versus normal tissue (log2 FC = +0.923, t-test p < 0.001).
This table shows molecular features associated with SEPHS1 in patient tissues and cancer cell lines. In patient samples, SEPHS1 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, SEPHS1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
