Q-omics provides the consensus-scored DYNLRB2 profile across patient tissues and cancer cell-line models. DYNLRB2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, DYNLRB2 is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, DYNLRB2 RNA expression shows 17,891 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight UVM, KICH, and BRCA as cancer lineages where DYNLRB2 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 DYNLRB2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DYNLRB2 survival associations across molecular data types. DYNLRB2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (2) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DYNLRB2 RNA expression–survival associations across cancer types. High DYNLRB2 expression shows favorable associations in UVM, ACC, BLCA, BRCA, UCEC and KIRC. The UVM 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 UVM as the clearest survival context for DYNLRB2 RNA expression.
This table summarizes DYNLRB2 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 2. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for DYNLRB2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DYNLRB2 shows lower tumor expression in KICH, THCA, STAD, LUAD, LUSC and KIRC. The KICH box plot shows higher DYNLRB2 RNA expression in normal versus tumor tissue (log2 FC = −2.571, t-test p < 0.001).
This table shows molecular features associated with DYNLRB2 in patient tissues and cancer cell lines. In patient samples, DYNLRB2 shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, DYNLRB2 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 OVARY and BREAST.
