Q-omics provides the consensus-scored ATG3 profile across patient tissues and cancer cell-line models. ATG3 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ATG3 is differentially expressed in 13, with the highest sampling consensus in LIHC. Additionally, ATG3 protein abundance shows 21,481 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, LIHC, and PDAC as cancer lineages where ATG3 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 ATG3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATG3 survival associations across molecular data types. ATG3 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATG3 RNA expression–survival associations across cancer types. High ATG3 expression shows unfavorable associations in ACC, LIHC, KICH and LAML, but favorable associations in KIRC 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 ATG3 RNA expression.
This table summarizes ATG3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 3. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ATG3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATG3 shows lower tumor expression in THCA and higher tumor expression in LIHC, HNSC, STAD, COAD and BLCA. The LIHC box plot shows higher ATG3 RNA expression in tumor versus normal tissue (log2 FC = +0.991, t-test p < 0.001).
This table shows molecular features associated with ATG3 in patient tissues and cancer cell lines. In patient samples, ATG3 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, ATG3 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 PANCREAS and UPPER_AERODIGESTIVE_TRACT.
