Q-omics provides the consensus-scored DNM1L profile across patient tissues and cancer cell-line models. DNM1L expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, DNM1L is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, DNM1L protein abundance shows 27,507 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, HNSC, and GBM as cancer lineages where DNM1L 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 DNM1L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DNM1L survival associations across molecular data types. DNM1L RNA expression shows survival associations in the most cancer types (23), 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 DNM1L RNA expression–survival associations across cancer types. High DNM1L expression shows unfavorable associations in LIHC, MESO, CESC, UVM, HNSC and LUAD. The LIHC 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 LIHC as the clearest survival context for DNM1L RNA expression.
This table summarizes DNM1L 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 8. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DNM1L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DNM1L shows lower tumor expression in THCA and KIRC and higher tumor expression in HNSC, LIHC, LUSC and LUAD. The HNSC box plot shows higher DNM1L RNA expression in tumor versus normal tissue (log2 FC = +0.827, t-test p < 0.001).
This table shows molecular features associated with DNM1L in patient tissues and cancer cell lines. In patient samples, DNM1L 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, DNM1L 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_Myeloma and BLOOD_Lymphoma.
