aquaporin 3 (Gill blood group)Genealiases: AQP-3 · GIL
Q-omics provides the consensus-scored AQP3 profile across patient tissues and cancer cell-line models. AQP3 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, AQP3 is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, AQP3 RNA expression shows 16,469 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UVM, and KIRP as cancer lineages where AQP3 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 AQP3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AQP3 survival associations across molecular data types. AQP3 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) 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 AQP3 RNA expression–survival associations across cancer types. High AQP3 expression shows unfavorable associations in UVM, ACC and LGG, but favorable associations in KIRC, CESC and THYM. The UVM 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 UVM as the clearest survival context for AQP3 RNA expression.
This table summarizes AQP3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for AQP3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AQP3 shows lower tumor expression in KIRP, HNSC, KIRC, KICH, LIHC and LUSC. The KIRP box plot shows higher AQP3 RNA expression in normal versus tumor tissue (log2 FC = −4.373, t-test p < 0.001).
This table shows molecular features associated with AQP3 in patient tissues and cancer cell lines. In patient samples, AQP3 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, AQP3 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 CNS and BLOOD_Leukemia.
