Q-omics provides the consensus-scored GGPS1 profile across patient tissues and cancer cell-line models. GGPS1 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, GGPS1 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, GGPS1 RNA expression shows 20,198 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, HNSC, and ACC as cancer lineages where GGPS1 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 GGPS1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GGPS1 survival associations across molecular data types. GGPS1 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (3) 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 GGPS1 RNA expression–survival associations across cancer types. High GGPS1 expression shows unfavorable associations in UVM, ACC, KIRP, HNSC, SCLC and LIHC. The UVM 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 UVM as the clearest survival context for GGPS1 RNA expression.
This table summarizes GGPS1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for GGPS1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GGPS1 shows lower tumor expression in KICH and THCA and higher tumor expression in HNSC, LIHC, LUAD and BRCA. The HNSC box plot shows higher GGPS1 RNA expression in tumor versus normal tissue (log2 FC = +0.678, t-test p < 0.001).
This table shows molecular features associated with GGPS1 in patient tissues and cancer cell lines. In patient samples, GGPS1 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, GGPS1 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 BREAST and BLOOD_Leukemia.
