Q-omics provides the consensus-scored PRKACG profile across patient tissues and cancer cell-line models. PRKACG expression is associated with patient survival in 13 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, PRKACG is differentially expressed in 5, with the highest sampling consensus in KIRC. Additionally, PRKACG RNA expression shows 10,221 significant gene co-expression associations, with the highest sampling consensus in ESCA. Together, these results highlight LIHC, KIRC, and ESCA as cancer lineages where PRKACG 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 PRKACG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PRKACG survival associations across molecular data types. PRKACG RNA expression shows survival associations in the most cancer types (13), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PRKACG RNA expression–survival associations across cancer types. High PRKACG expression shows unfavorable associations in LIHC, LUSC, KIRC and BLCA, but favorable associations in SKCM and LGG. The LIHC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .004). Together, the overview and detailed table identify LIHC as the clearest survival context for PRKACG RNA expression.
This table summarizes PRKACG tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for PRKACG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PRKACG shows lower tumor expression in KIRC, PRAD, KICH and READ and higher tumor expression in STAD. The KIRC box plot shows higher PRKACG RNA expression in normal versus tumor tissue (log2 FC = −0.017, t-test p = .008).
This table shows molecular features associated with PRKACG in patient tissues and cancer cell lines. In patient samples, PRKACG shows the broadest associations at the RNA and protein expression levels, with ESCA recurring as the lineage with the largest associated feature set. In cancer cell lines, PRKACG 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 BLOOD_Myeloma and OVARY.
