Q-omics provides the consensus-scored SGCD profile across patient tissues and cancer cell-line models. SGCD expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, SGCD is differentially expressed in 12, with the highest sampling consensus in THCA. Additionally, SGCD protein abundance shows 26,151 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight UVM, THCA, and HNSC as cancer lineages where SGCD 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 SGCD — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SGCD survival associations across molecular data types. SGCD RNA expression shows survival associations in the most cancer types (22), followed by mutation status (6) 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 SGCD RNA expression–survival associations across cancer types. High SGCD expression shows unfavorable associations in UVM, BLCA and SCLC, but favorable associations in LGG, KIRC and UCS. The UVM 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 UVM as the clearest survival context for SGCD RNA expression.
This table summarizes SGCD 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 COAD for protein.
This table ranks reproducible tumor–normal expression differences for SGCD. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SGCD shows lower tumor expression in THCA, BLCA, LUSC, KIRC, KICH and UCEC. The THCA box plot shows higher SGCD RNA expression in normal versus tumor tissue (log2 FC = −2.377, t-test p < 0.001).
This table shows molecular features associated with SGCD in patient tissues and cancer cell lines. In patient samples, SGCD 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, SGCD RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and SKIN.
