angiogenic factor with G-patch and FHA domains 1Genealiases: GPATC7 · GPATCH7 · HSU84971 · HUS84971 · VG5Q
Q-omics provides the consensus-scored AGGF1 profile across patient tissues and cancer cell-line models. AGGF1 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, AGGF1 is differentially expressed in 12, with the highest sampling consensus in LIHC. Additionally, AGGF1 RNA expression shows 21,041 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, LIHC, and ACC as cancer lineages where AGGF1 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 AGGF1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGGF1 survival associations across molecular data types. AGGF1 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (1) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AGGF1 RNA expression–survival associations across cancer types. High AGGF1 expression shows unfavorable associations in UVM, KICH, LIHC and LGG, but favorable associations in KIRC and READ. 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 AGGF1 RNA expression.
This table summarizes AGGF1 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 3. The strongest signals are observed in LIHC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for AGGF1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGGF1 shows lower tumor expression in THCA and higher tumor expression in LIHC, HNSC, BLCA, BRCA and CHOL. The LIHC box plot shows higher AGGF1 RNA expression in tumor versus normal tissue (log2 FC = +0.949, t-test p < 0.001).
This table shows molecular features associated with AGGF1 in patient tissues and cancer cell lines. In patient samples, AGGF1 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, AGGF1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and UPPER_AERODIGESTIVE_TRACT.
