angiogenic factor with G-patch and FHA domains 1 pseudogene 2Genealiases: []
Q-omics provides the consensus-scored AGGF1P2 profile across patient tissues and cancer cell-line models. AGGF1P2 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, AGGF1P2 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, AGGF1P2 RNA expression shows 17,647 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRC, HNSC, and THYM as cancer lineages where AGGF1P2 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 AGGF1P2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGGF1P2 survival associations across molecular data types. AGGF1P2 RNA expression shows survival associations in the most cancer types (22). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AGGF1P2 RNA expression–survival associations across cancer types. High AGGF1P2 expression shows unfavorable associations in KIRC, ESCA and ACC, but favorable associations in KIRP, UVM and LGG. The KIRC 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 KIRC as the clearest survival context for AGGF1P2 RNA expression.
This table summarizes AGGF1P2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for AGGF1P2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGGF1P2 shows lower tumor expression in BRCA and higher tumor expression in HNSC, LUAD, KIRP, LUSC and KIRC. The HNSC box plot shows higher AGGF1P2 RNA expression in tumor versus normal tissue (log2 FC = +0.321, t-test p < 0.001).
This table shows molecular features associated with AGGF1P2 in patient tissues and cancer cell lines. In patient samples, AGGF1P2 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set.
