SKI like proto-oncogeneGenealiases: SNO · SnoA · SnoI · SnoN
Q-omics provides the consensus-scored SKIL profile across patient tissues and cancer cell-line models. SKIL expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, SKIL is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, SKIL RNA expression shows 19,623 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRP, HNSC, and UVM as cancer lineages where SKIL 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 SKIL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SKIL survival associations across molecular data types. SKIL RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) 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 SKIL RNA expression–survival associations across cancer types. High SKIL expression shows unfavorable associations in KIRP, KICH, CESC, ACC and THCA, but favorable associations in SKCM. The KIRP 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 KIRP as the clearest survival context for SKIL RNA expression.
This table summarizes SKIL 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 6. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SKIL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SKIL shows lower tumor expression in KICH and higher tumor expression in HNSC, BLCA, STAD, BRCA and CHOL. The HNSC box plot shows higher SKIL RNA expression in tumor versus normal tissue (log2 FC = +1.609, t-test p < 0.001).
This table shows molecular features associated with SKIL in patient tissues and cancer cell lines. In patient samples, SKIL 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, SKIL 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 SOFT_TISSUE and BLOOD_Lymphoma.
