Q-omics provides the consensus-scored SCGN profile across patient tissues and cancer cell-line models. SCGN expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SCGN is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, SCGN RNA expression shows 12,953 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, COAD, and TGCT as cancer lineages where SCGN 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 SCGN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SCGN survival associations across molecular data types. SCGN RNA expression shows survival associations in the most cancer types (20), followed by mutation status (7) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SCGN RNA expression–survival associations across cancer types. High SCGN expression shows unfavorable associations in UVM, LIHC and UCEC, but favorable associations in KIRC, ACC 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 SCGN RNA expression.
This table summarizes SCGN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 3. The strongest signals are observed in COAD for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SCGN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SCGN shows lower tumor expression in COAD, KICH, READ and STAD and higher tumor expression in KIRC and KIRP. The COAD box plot shows higher SCGN RNA expression in normal versus tumor tissue (log2 FC = −4.108, t-test p < 0.001).
This table shows molecular features associated with SCGN in patient tissues and cancer cell lines. In patient samples, SCGN 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, SCGN RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in LIVER and LUNG_SCLC.
