Q-omics provides the consensus-scored GFAP profile across patient tissues and cancer cell-line models. GFAP expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, GFAP is differentially expressed in 8, with the highest sampling consensus in KICH. Additionally, GFAP RNA expression shows 10,509 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UCEC, KICH, and TGCT as cancer lineages where GFAP 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 GFAP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GFAP survival associations across molecular data types. GFAP RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GFAP RNA expression–survival associations across cancer types. High GFAP expression shows unfavorable associations in UCEC, LGG, STAD, MESO and HNSC, but favorable associations in BRCA. The UCEC 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 UCEC as the clearest survival context for GFAP RNA expression.
This table summarizes GFAP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 2. The strongest signals are observed in LUAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for GFAP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GFAP shows lower tumor expression in BRCA and higher tumor expression in KICH, LUAD, KIRC, THCA and LUSC. The KICH box plot shows higher GFAP RNA expression in tumor versus normal tissue (log2 FC = +2.200, t-test p < 0.001).
This table shows molecular features associated with GFAP in patient tissues and cancer cell lines. In patient samples, GFAP 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, GFAP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
