Q-omics provides the consensus-scored MLH1 profile across patient tissues and cancer cell-line models. MLH1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, MLH1 is differentially expressed in 9, with the highest sampling consensus in THCA. Additionally, MLH1 protein abundance shows 25,894 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SCLC, THCA, and GBM as cancer lineages where MLH1 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 MLH1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MLH1 survival associations across molecular data types. MLH1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (8) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MLH1 RNA expression–survival associations across cancer types. High MLH1 expression shows unfavorable associations in ACC, KICH and BLCA, but favorable associations in SCLC, UCS and KIRC. The SCLC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify SCLC as the clearest survival context for MLH1 RNA expression.
This table summarizes MLH1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for MLH1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MLH1 shows lower tumor expression in THCA, KIRC, LUSC and KICH and higher tumor expression in LIHC and BRCA. The THCA box plot shows higher MLH1 RNA expression in normal versus tumor tissue (log2 FC = −0.802, t-test p < 0.001).
This table shows molecular features associated with MLH1 in patient tissues and cancer cell lines. In patient samples, MLH1 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, MLH1 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 UPPER_AERODIGESTIVE_TRACT and BLOOD_Lymphoma.
