Q-omics provides the consensus-scored SPOP profile across patient tissues and cancer cell-line models. SPOP expression is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, SPOP is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, SPOP RNA expression shows 20,303 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRP, HNSC, and UVM as cancer lineages where SPOP 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 SPOP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SPOP survival associations across molecular data types. SPOP RNA expression shows survival associations in the most cancer types (30), 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 SPOP RNA expression–survival associations across cancer types. High SPOP expression shows unfavorable associations in KIRP, KICH and HNSC, but favorable associations in KIRC, LUAD and BRCA. The KIRP Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify KIRP as the clearest survival context for SPOP RNA expression.
This table summarizes SPOP 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 3. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for SPOP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SPOP shows lower tumor expression in THCA, KICH and BLCA and higher tumor expression in HNSC, KIRP and LIHC. The HNSC box plot shows higher SPOP RNA expression in tumor versus normal tissue (log2 FC = +0.485, t-test p < 0.001).
This table shows molecular features associated with SPOP in patient tissues and cancer cell lines. In patient samples, SPOP shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, SPOP 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 LUNG_NSCLC_LUAD and UPPER_AERODIGESTIVE_TRACT.
