Q-omics provides the consensus-scored MLEC profile across patient tissues and cancer cell-line models. MLEC expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, MLEC is differentially expressed in 16, with the highest sampling consensus in KIRC. Additionally, MLEC protein abundance shows 31,208 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LGG, KIRC, and GBM as cancer lineages where MLEC 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 MLEC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MLEC survival associations across molecular data types. MLEC RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MLEC RNA expression–survival associations across cancer types. High MLEC expression shows unfavorable associations in LGG, LIHC, ESCA and HNSC, but favorable associations in SCLC and KIRC. The LGG 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 LGG as the clearest survival context for MLEC RNA expression.
This table summarizes MLEC tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 10. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MLEC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MLEC shows lower tumor expression in THCA and higher tumor expression in KIRC, HNSC, STAD, LIHC and KIRP. The KIRC box plot shows higher MLEC RNA expression in tumor versus normal tissue (log2 FC = +1.251, t-test p < 0.001).
This table shows molecular features associated with MLEC in patient tissues and cancer cell lines. In patient samples, MLEC 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, MLEC RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
