autophagy related 4A cysteine peptidaseGenealiases: APG4A · AUTL2 · HsAPG4A
Q-omics provides the consensus-scored ATG4A profile across patient tissues and cancer cell-line models. ATG4A expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ATG4A is differentially expressed in 8, with the highest sampling consensus in COAD. Additionally, ATG4A RNA expression shows 18,820 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, COAD, and UVM as cancer lineages where ATG4A 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 ATG4A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATG4A survival associations across molecular data types. ATG4A RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) 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 ATG4A RNA expression–survival associations across cancer types. High ATG4A expression shows unfavorable associations in BRCA, LGG, MESO and HNSC, but favorable associations in KIRC and LUAD. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for ATG4A RNA expression.
This table summarizes ATG4A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 5. The strongest signals are observed in COAD for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for ATG4A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATG4A shows lower tumor expression in COAD, THCA, LUSC and KIRP and higher tumor expression in LIHC and BRCA. The COAD box plot shows higher ATG4A RNA expression in normal versus tumor tissue (log2 FC = −0.803, t-test p < 0.001).
This table shows molecular features associated with ATG4A in patient tissues and cancer cell lines. In patient samples, ATG4A 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, ATG4A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Lymphoma.
