Q-omics provides the consensus-scored SEPSECS profile across patient tissues and cancer cell-line models. SEPSECS expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SEPSECS is differentially expressed in 7, with the highest sampling consensus in KICH. Additionally, SEPSECS RNA expression shows 21,317 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, KICH, and UVM as cancer lineages where SEPSECS 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 SEPSECS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SEPSECS survival associations across molecular data types. SEPSECS RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SEPSECS RNA expression–survival associations across cancer types. High SEPSECS expression shows unfavorable associations in LGG and UVM, but favorable associations in KIRC, KIRP, UCS and HNSC. The KIRC 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 KIRC as the clearest survival context for SEPSECS RNA expression.
This table summarizes SEPSECS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SEPSECS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SEPSECS shows lower tumor expression in KICH, THCA, COAD and LUSC and higher tumor expression in STAD and LUAD. The KICH box plot shows higher SEPSECS RNA expression in normal versus tumor tissue (log2 FC = −1.472, t-test p < 0.001).
This table shows molecular features associated with SEPSECS in patient tissues and cancer cell lines. In patient samples, SEPSECS shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, SEPSECS 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 BONE and BLOOD_Leukemia.
