Q-omics provides the consensus-scored GSKIP profile across patient tissues and cancer cell-line models. GSKIP expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, GSKIP is differentially expressed in 14, with the highest sampling consensus in KIRP. Additionally, GSKIP RNA expression shows 19,615 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KIRP as cancer lineages where GSKIP 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 GSKIP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GSKIP survival associations across molecular data types. GSKIP RNA expression shows survival associations in the most cancer types (26), followed by mutation status (2) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GSKIP RNA expression–survival associations across cancer types. High GSKIP expression shows unfavorable associations in ACC, UVM, ESCA and LIHC, but favorable associations in UCEC and COAD. The ACC 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 ACC as the clearest survival context for GSKIP RNA expression.
This table summarizes GSKIP 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 KIRP for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for GSKIP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GSKIP shows lower tumor expression in KICH and KIRC and higher tumor expression in KIRP, LIHC, UCEC and BRCA. The KIRP box plot shows higher GSKIP RNA expression in tumor versus normal tissue (log2 FC = +0.816, t-test p < 0.001).
This table shows molecular features associated with GSKIP in patient tissues and cancer cell lines. In patient samples, GSKIP shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, GSKIP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Leukemia.
