Q-omics provides the consensus-scored SGCA profile across patient tissues and cancer cell-line models. SGCA expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in LUSC. Among the 18 cancer types available for tumor–normal comparison, SGCA is differentially expressed in 14, with the highest sampling consensus in BLCA. Additionally, SGCA RNA expression shows 22,270 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight LUSC, BLCA, and LUAD as cancer lineages where SGCA 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 SGCA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SGCA survival associations across molecular data types. SGCA RNA expression shows survival associations in the most cancer types (21), followed by mutation status (9) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SGCA RNA expression–survival associations across cancer types. High SGCA expression shows unfavorable associations in LUSC, LGG, LAML and KIRP, but favorable associations in KIRC and UVM. The LUSC 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 LUSC as the clearest survival context for SGCA RNA expression.
This table summarizes SGCA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 3. The strongest signals are observed in LUAD for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for SGCA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SGCA shows lower tumor expression in BLCA, LUAD, COAD, LUSC, HNSC and STAD. The BLCA box plot shows higher SGCA RNA expression in normal versus tumor tissue (log2 FC = −5.197, t-test p < 0.001).
This table shows molecular features associated with SGCA in patient tissues and cancer cell lines. In patient samples, SGCA shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, SGCA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in CNS and OVARY.
