Q-omics provides the consensus-scored SPEG profile across patient tissues and cancer cell-line models. SPEG expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SPEG is differentially expressed in 13, with the highest sampling consensus in BLCA. Additionally, SPEG protein abundance shows 20,375 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KIRC, BLCA, and HNSC as cancer lineages where SPEG 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 SPEG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SPEG survival associations across molecular data types. SPEG RNA expression shows survival associations in the most cancer types (26), followed by mutation status (7) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SPEG RNA expression–survival associations across cancer types. High SPEG expression shows unfavorable associations in KIRC, UCEC, HNSC, BLCA and KIRP, but favorable associations in ESCA. The KIRC 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 KIRC as the clearest survival context for SPEG RNA expression.
This table summarizes SPEG 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 5. The strongest signals are observed in BLCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SPEG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SPEG shows lower tumor expression in BLCA, COAD, UCEC, BRCA, STAD and READ. The BLCA box plot shows higher SPEG RNA expression in normal versus tumor tissue (log2 FC = −4.287, t-test p < 0.001).
This table shows molecular features associated with SPEG in patient tissues and cancer cell lines. In patient samples, SPEG 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, SPEG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
