Q-omics provides the consensus-scored SEZ6 profile across patient tissues and cancer cell-line models. SEZ6 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SEZ6 is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, SEZ6 RNA expression shows 15,990 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, COAD, and TGCT as cancer lineages where SEZ6 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 SEZ6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SEZ6 survival associations across molecular data types. SEZ6 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (5) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SEZ6 RNA expression–survival associations across cancer types. High SEZ6 expression shows unfavorable associations in KIRC, ACC and COAD, but favorable associations in BRCA, PAAD and LGG. 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 SEZ6 RNA expression.
This table summarizes SEZ6 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 2. The strongest signals are observed in COAD for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SEZ6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SEZ6 shows lower tumor expression in COAD, KIRP, READ, THCA and LUAD and higher tumor expression in LIHC. The COAD box plot shows higher SEZ6 RNA expression in normal versus tumor tissue (log2 FC = −0.425, t-test p < 0.001).
This table shows molecular features associated with SEZ6 in patient tissues and cancer cell lines. In patient samples, SEZ6 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, SEZ6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
