Q-omics provides the consensus-scored DYNC2LI1 profile across patient tissues and cancer cell-line models. DYNC2LI1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, DYNC2LI1 is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, DYNC2LI1 RNA expression shows 20,509 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, KICH, and ACC as cancer lineages where DYNC2LI1 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 DYNC2LI1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DYNC2LI1 survival associations across molecular data types. DYNC2LI1 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and 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 DYNC2LI1 RNA expression–survival associations across cancer types. High DYNC2LI1 expression shows unfavorable associations in LGG and LIHC, but favorable associations in KIRC, READ, KIRP and MESO. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for DYNC2LI1 RNA expression.
This table summarizes DYNC2LI1 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 3. The strongest signals are observed in KICH for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for DYNC2LI1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DYNC2LI1 shows lower tumor expression in KICH, THCA and LUSC and higher tumor expression in LIHC, HNSC and BLCA. The KICH box plot shows higher DYNC2LI1 RNA expression in normal versus tumor tissue (log2 FC = −2.575, t-test p < 0.001).
This table shows molecular features associated with DYNC2LI1 in patient tissues and cancer cell lines. In patient samples, DYNC2LI1 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, DYNC2LI1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
