Q-omics provides the consensus-scored SVIL profile across patient tissues and cancer cell-line models. SVIL expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, SVIL is differentially expressed in 14, with the highest sampling consensus in BLCA. Additionally, SVIL protein abundance shows 25,858 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight BLCA, and GBM as cancer lineages where SVIL 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 SVIL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SVIL survival associations across molecular data types. SVIL RNA expression shows survival associations in the most cancer types (26), followed by mutation status (8) 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 SVIL RNA expression–survival associations across cancer types. High SVIL expression shows unfavorable associations in BLCA, MESO and LGG, but favorable associations in KIRC, UVM and HNSC. The BLCA 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 BLCA as the clearest survival context for SVIL RNA expression.
This table summarizes SVIL 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 7. The strongest signals are observed in BLCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SVIL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SVIL shows lower tumor expression in BLCA, COAD, UCEC and BRCA and higher tumor expression in LIHC and KIRP. The BLCA box plot shows higher SVIL RNA expression in normal versus tumor tissue (log2 FC = −1.943, t-test p = .003).
This table shows molecular features associated with SVIL in patient tissues and cancer cell lines. In patient samples, SVIL 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, SVIL 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 SOFT_TISSUE and BLOOD_Leukemia.
