Q-omics provides the consensus-scored DYNC1I2 profile across patient tissues and cancer cell-line models. DYNC1I2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, DYNC1I2 is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, DYNC1I2 protein abundance shows 21,678 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRC, HNSC, and PDAC as cancer lineages where DYNC1I2 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 DYNC1I2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DYNC1I2 survival associations across molecular data types. DYNC1I2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) 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 DYNC1I2 RNA expression–survival associations across cancer types. High DYNC1I2 expression shows unfavorable associations in MESO, ACC, UVM, HNSC and LIHC, but favorable associations in KIRC. 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 DYNC1I2 RNA expression.
This table summarizes DYNC1I2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DYNC1I2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DYNC1I2 shows lower tumor expression in KICH and higher tumor expression in HNSC, LIHC, LUAD, CHOL and BRCA. The HNSC box plot shows higher DYNC1I2 RNA expression in tumor versus normal tissue (log2 FC = +0.626, t-test p < 0.001).
This table shows molecular features associated with DYNC1I2 in patient tissues and cancer cell lines. In patient samples, DYNC1I2 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, DYNC1I2 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 SOFT_TISSUE and BLOOD_Leukemia.
