Q-omics provides the consensus-scored AVP profile across patient tissues and cancer cell-line models. AVP expression is associated with patient survival in 15 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, AVP is differentially expressed in 9, with the highest sampling consensus in KIRP. Additionally, AVP RNA expression shows 7,894 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight STAD, KIRP, and TGCT as cancer lineages where AVP 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 AVP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AVP survival associations across molecular data types. AVP RNA expression shows survival associations in the most cancer types (15), followed by mutation status (2) 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 AVP RNA expression–survival associations across cancer types. High AVP expression shows unfavorable associations in STAD, COAD, UCS and KIRC, but favorable associations in CESC and LUAD. The STAD Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify STAD as the clearest survival context for AVP RNA expression.
This table summarizes AVP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 2. The strongest signals are observed in KIRP for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for AVP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AVP shows lower tumor expression in KIRP, BRCA, KIRC, PRAD, KICH and CHOL. The KIRP box plot shows higher AVP RNA expression in normal versus tumor tissue (log2 FC = −0.397, t-test p < 0.001).
This table shows molecular features associated with AVP in patient tissues and cancer cell lines. In patient samples, AVP 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, AVP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Leukemia.
