Q-omics provides the consensus-scored SGCB profile across patient tissues and cancer cell-line models. SGCB 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, SGCB is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, SGCB protein abundance shows 22,337 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KIRC, and HNSC as cancer lineages where SGCB 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 SGCB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SGCB survival associations across molecular data types. SGCB RNA expression shows survival associations in the most cancer types (20), followed by mutation status (3) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SGCB RNA expression–survival associations across cancer types. High SGCB expression shows unfavorable associations in BLCA, UVM and MESO, but favorable associations in KIRC, UCEC and SKCM. 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 SGCB RNA expression.
This table summarizes SGCB 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 5. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SGCB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SGCB shows lower tumor expression in KICH, BLCA, LUSC, LUAD and UCEC and higher tumor expression in KIRC. The KIRC box plot shows higher SGCB RNA expression in tumor versus normal tissue (log2 FC = +1.114, t-test p < 0.001).
This table shows molecular features associated with SGCB in patient tissues and cancer cell lines. In patient samples, SGCB 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, SGCB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BONE and LARGE_INTESTINE.
