Q-omics provides the consensus-scored SGSH profile across patient tissues and cancer cell-line models. SGSH expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, SGSH is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, SGSH RNA expression shows 19,386 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight LGG, KIRP, and THYM as cancer lineages where SGSH 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 SGSH — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SGSH survival associations across molecular data types. SGSH RNA expression shows survival associations in the most cancer types (25), followed by mutation status (8) 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 SGSH RNA expression–survival associations across cancer types. High SGSH expression shows unfavorable associations in LGG, LIHC, UVM and SKCM, but favorable associations in PAAD and CHOL. The LGG 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 LGG as the clearest survival context for SGSH RNA expression.
This table summarizes SGSH tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRP for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SGSH. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SGSH shows higher tumor expression in KIRP, HNSC, LIHC, KIRC, CHOL and STAD. The KIRP box plot shows higher SGSH RNA expression in tumor versus normal tissue (log2 FC = +0.997, t-test p < 0.001).
This table shows molecular features associated with SGSH in patient tissues and cancer cell lines. In patient samples, SGSH shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, SGSH 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 BONE and BLOOD_Lymphoma.
