Q-omics provides the consensus-scored AGO3 profile across patient tissues and cancer cell-line models. AGO3 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, AGO3 is differentially expressed in 7, with the highest sampling consensus in HNSC. Additionally, AGO3 protein abundance shows 29,044 significant protein co-abundance associations, with the highest sampling consensus in UCEC. Together, these results highlight ACC, HNSC, and UCEC as cancer lineages where AGO3 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 AGO3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGO3 survival associations across molecular data types. AGO3 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AGO3 RNA expression–survival associations across cancer types. High AGO3 expression shows unfavorable associations in ACC, LIHC and CESC, but favorable associations in SCLC, HNSC and UCS. The ACC 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 ACC as the clearest survival context for AGO3 RNA expression.
This table summarizes AGO3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 10. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for AGO3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGO3 shows lower tumor expression in THCA, KICH, BRCA and UCEC and higher tumor expression in HNSC and CHOL. The HNSC box plot shows higher AGO3 RNA expression in tumor versus normal tissue (log2 FC = +0.629, t-test p < 0.001).
This table shows molecular features associated with AGO3 in patient tissues and cancer cell lines. In patient samples, AGO3 shows the broadest associations at the RNA and protein expression levels, with UCEC recurring as the lineage with the largest associated feature set. In cancer cell lines, AGO3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
