Q-omics provides the consensus-scored SCEL profile across patient tissues and cancer cell-line models. SCEL expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SCEL is differentially expressed in 12, with the highest sampling consensus in THCA. Additionally, SCEL protein abundance shows 18,397 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight ACC, THCA, and HNSC as cancer lineages where SCEL 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 SCEL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SCEL survival associations across molecular data types. SCEL RNA expression shows survival associations in the most cancer types (21), followed by mutation status (7) 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 SCEL RNA expression–survival associations across cancer types. High SCEL expression shows unfavorable associations in ACC, PAAD, UCEC, SKCM and BRCA, but favorable associations in KIRP. 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 SCEL RNA expression.
This table summarizes SCEL 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 7. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SCEL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SCEL shows lower tumor expression in HNSC, LUSC, LUAD and KICH and higher tumor expression in THCA and UCEC. The THCA box plot shows higher SCEL RNA expression in tumor versus normal tissue (log2 FC = +4.346, t-test p < 0.001).
This table shows molecular features associated with SCEL in patient tissues and cancer cell lines. In patient samples, SCEL 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, SCEL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
