Q-omics provides the consensus-scored ATG4C profile across patient tissues and cancer cell-line models. ATG4C expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ATG4C is differentially expressed in 13, with the highest sampling consensus in THCA. Additionally, ATG4C protein abundance shows 22,195 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, THCA, and LSCC as cancer lineages where ATG4C 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 ATG4C — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATG4C survival associations across molecular data types. ATG4C RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATG4C RNA expression–survival associations across cancer types. High ATG4C expression shows unfavorable associations in LGG, LIHC, KIRP and CESC, but favorable associations in KIRC and READ. 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 ATG4C RNA expression.
This table summarizes ATG4C 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 6. The strongest signals are observed in THCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for ATG4C. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATG4C shows lower tumor expression in THCA, KICH and KIRP and higher tumor expression in LIHC, HNSC and STAD. The THCA box plot shows higher ATG4C RNA expression in normal versus tumor tissue (log2 FC = −1.125, t-test p < 0.001).
This table shows molecular features associated with ATG4C in patient tissues and cancer cell lines. In patient samples, ATG4C shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ATG4C RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
