Q-omics provides the consensus-scored GIP profile across patient tissues and cancer cell-line models. GIP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, GIP is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, GIP RNA expression shows 10,125 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, HNSC, and TGCT as cancer lineages where GIP 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 GIP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GIP survival associations across molecular data types. GIP RNA expression shows survival associations in the most cancer types (24), followed by mutation status (1) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GIP RNA expression–survival associations across cancer types. High GIP expression shows unfavorable associations in KIRC, LIHC, LUAD, KIRP, OV and CESC. 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 GIP RNA expression.
This table summarizes GIP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9. The strongest signals are observed in LUAD for RNA.
This table ranks reproducible tumor–normal expression differences for GIP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GIP shows lower tumor expression in STAD and higher tumor expression in HNSC, LUAD, KIRP, THCA and BRCA. The HNSC box plot shows higher GIP RNA expression in tumor versus normal tissue (log2 FC = +0.225, t-test p = .017).
This table shows molecular features associated with GIP in patient tissues and cancer cell lines. In patient samples, GIP shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, GIP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BREAST.
