autophagy related 9AGenealiases: APG9L1 · MGD3208 · mATG9
Q-omics provides the consensus-scored ATG9A profile across patient tissues and cancer cell-line models. ATG9A expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ATG9A is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, ATG9A RNA expression shows 19,319 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where ATG9A 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 ATG9A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATG9A survival associations across molecular data types. ATG9A RNA expression shows survival associations in the most cancer types (23), followed by mutation status (8) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATG9A RNA expression–survival associations across cancer types. High ATG9A expression shows unfavorable associations in ACC, MESO, LIHC, OV, CESC and SKCM. 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 ATG9A RNA expression.
This table summarizes ATG9A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ATG9A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATG9A shows lower tumor expression in KICH and higher tumor expression in HNSC, LIHC, STAD, LUSC and KIRC. The HNSC box plot shows higher ATG9A RNA expression in tumor versus normal tissue (log2 FC = +0.701, t-test p < 0.001).
This table shows molecular features associated with ATG9A in patient tissues and cancer cell lines. In patient samples, ATG9A 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, ATG9A 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 LARGE_INTESTINE and CNS.
