Q-omics provides the consensus-scored DYNLT1 profile across patient tissues and cancer cell-line models. DYNLT1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, DYNLT1 is differentially expressed in 14, with the highest sampling consensus in KICH. Additionally, DYNLT1 RNA expression shows 18,087 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight MESO, KICH, and UVM as cancer lineages where DYNLT1 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 DYNLT1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DYNLT1 survival associations across molecular data types. DYNLT1 RNA expression shows survival associations in the most cancer types (24), followed by mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DYNLT1 RNA expression–survival associations across cancer types. High DYNLT1 expression shows unfavorable associations in MESO, LIHC, BRCA, ACC, ESCA and UCS. The MESO 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 MESO as the clearest survival context for DYNLT1 RNA expression.
This table summarizes DYNLT1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for DYNLT1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DYNLT1 shows lower tumor expression in KICH and THCA and higher tumor expression in LIHC, KIRP, BRCA and UCEC. The KICH box plot shows higher DYNLT1 RNA expression in normal versus tumor tissue (log2 FC = −1.764, t-test p < 0.001).
This table shows molecular features associated with DYNLT1 in patient tissues and cancer cell lines. In patient samples, DYNLT1 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, DYNLT1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BONE.
