Q-omics provides the consensus-scored PPARG profile across patient tissues and cancer cell-line models. PPARG expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PPARG is differentially expressed in 14, with the highest sampling consensus in THCA. Additionally, PPARG RNA expression shows 18,838 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, THCA, and GBM as cancer lineages where PPARG 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 PPARG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPARG survival associations across molecular data types. PPARG RNA expression shows survival associations in the most cancer types (22), followed by mutation status (7) 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 PPARG RNA expression–survival associations across cancer types. High PPARG expression shows unfavorable associations in LIHC, ACC and LGG, but favorable associations in KIRC, BLCA and UVM. 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 PPARG RNA expression.
This table summarizes PPARG 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 1. The strongest signals are observed in THCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for PPARG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPARG shows lower tumor expression in THCA, HNSC, COAD and LUAD and higher tumor expression in KIRP and LIHC. The THCA box plot shows higher PPARG RNA expression in normal versus tumor tissue (log2 FC = −1.526, t-test p < 0.001).
This table shows molecular features associated with PPARG in patient tissues and cancer cell lines. In patient samples, PPARG 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, PPARG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in CNS and BONE.
