Q-omics provides the consensus-scored DYNLL1 profile across patient tissues and cancer cell-line models. DYNLL1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, DYNLL1 is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, DYNLL1 protein abundance shows 24,824 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight UVM, KIRC, and HNSC as cancer lineages where DYNLL1 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 DYNLL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DYNLL1 survival associations across molecular data types. DYNLL1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) 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 DYNLL1 RNA expression–survival associations across cancer types. High DYNLL1 expression shows unfavorable associations in UVM, LUAD, KICH, LIHC, KIRP and MESO. The UVM 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 UVM as the clearest survival context for DYNLL1 RNA expression.
This table summarizes DYNLL1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DYNLL1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DYNLL1 shows higher tumor expression in KIRC, LIHC, LUSC, BRCA, KIRP and CHOL. The KIRC box plot shows higher DYNLL1 RNA expression in tumor versus normal tissue (log2 FC = +0.653, t-test p < 0.001).
This table shows molecular features associated with DYNLL1 in patient tissues and cancer cell lines. In patient samples, DYNLL1 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, DYNLL1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
