Q-omics provides the consensus-scored SVOP profile across patient tissues and cancer cell-line models. SVOP expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, SVOP is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, SVOP RNA expression shows 13,447 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LGG, COAD, and GBM as cancer lineages where SVOP 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 SVOP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SVOP survival associations across molecular data types. SVOP RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SVOP RNA expression–survival associations across cancer types. High SVOP expression shows unfavorable associations in READ, UVM, BLCA and KICH, but favorable associations in LGG and PAAD. The LGG 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 LGG as the clearest survival context for SVOP RNA expression.
This table summarizes SVOP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14. The strongest signals are observed in COAD for RNA.
This table ranks reproducible tumor–normal expression differences for SVOP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SVOP shows lower tumor expression in COAD, KIRP, CHOL and PRAD and higher tumor expression in LUSC and BRCA. The COAD box plot shows higher SVOP RNA expression in normal versus tumor tissue (log2 FC = −0.410, t-test p < 0.001).
This table shows molecular features associated with SVOP in patient tissues and cancer cell lines. In patient samples, SVOP shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, SVOP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and LUNG_NSCLC_LUSC.
