Q-omics provides the consensus-scored SGCE profile across patient tissues and cancer cell-line models. SGCE expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, SGCE is differentially expressed in 15, with the highest sampling consensus in LUAD. Additionally, SGCE protein abundance shows 22,703 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight MESO, LUAD, and LSCC as cancer lineages where SGCE 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 SGCE — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SGCE survival associations across molecular data types. SGCE RNA expression shows survival associations in the most cancer types (26), followed by mutation status (6) 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 SGCE RNA expression–survival associations across cancer types. High SGCE expression shows unfavorable associations in MESO, STAD, ACC and KIRP, but favorable associations in SKCM and BRCA. The MESO 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 MESO as the clearest survival context for SGCE RNA expression.
This table summarizes SGCE 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 10. The strongest signals are observed in THCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SGCE. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SGCE shows lower tumor expression in LUAD, COAD, THCA, BLCA, KICH and LUSC. The LUAD box plot shows higher SGCE RNA expression in normal versus tumor tissue (log2 FC = −1.761, t-test p < 0.001).
This table shows molecular features associated with SGCE in patient tissues and cancer cell lines. In patient samples, SGCE shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, SGCE RNA and mutation anchors are most strongly linked to RNA-expression features, especially in STOMACH, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
