Q-omics provides the consensus-scored GGACT profile across patient tissues and cancer cell-line models. GGACT expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, GGACT is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, GGACT protein abundance shows 31,102 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, KIRP, and GBM as cancer lineages where GGACT 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 GGACT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GGACT survival associations across molecular data types. GGACT RNA expression shows survival associations in the most cancer types (27), followed by mutation status (1) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GGACT RNA expression–survival associations across cancer types. High GGACT expression shows unfavorable associations in LGG, UVM and LUAD, but favorable associations in KIRC, UCEC and SCLC. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for GGACT RNA expression.
This table summarizes GGACT tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 11. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GGACT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GGACT shows lower tumor expression in KIRP, KIRC and KICH and higher tumor expression in LUAD, STAD and COAD. The KIRP box plot shows higher GGACT RNA expression in normal versus tumor tissue (log2 FC = −3.566, t-test p < 0.001).
This table shows molecular features associated with GGACT in patient tissues and cancer cell lines. In patient samples, GGACT shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, GGACT 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 OVARY and BLOOD_Leukemia.
