Q-omics provides the consensus-scored MAP1LC3A profile across patient tissues and cancer cell-line models. MAP1LC3A expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, MAP1LC3A is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, MAP1LC3A protein abundance shows 29,923 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, THCA, and GBM as cancer lineages where MAP1LC3A 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 MAP1LC3A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MAP1LC3A survival associations across molecular data types. MAP1LC3A RNA expression shows survival associations in the most cancer types (26), followed by mutation status (3) 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 MAP1LC3A RNA expression–survival associations across cancer types. High MAP1LC3A expression shows unfavorable associations in ACC, LGG and OV, but favorable associations in KIRP, PAAD and HNSC. The KIRP Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .002). Together, the overview and detailed table identify KIRP as the clearest survival context for MAP1LC3A RNA expression.
This table summarizes MAP1LC3A 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 6. The strongest signals are observed in THCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for MAP1LC3A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MAP1LC3A shows lower tumor expression in THCA, KIRP, BLCA, LUAD, KIRC and HNSC. The THCA box plot shows higher MAP1LC3A RNA expression in normal versus tumor tissue (log2 FC = −2.102, t-test p < 0.001).
This table shows molecular features associated with MAP1LC3A in patient tissues and cancer cell lines. In patient samples, MAP1LC3A shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, MAP1LC3A 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 KIDNEY and CNS.
