Q-omics provides the consensus-scored DNAH9 profile across patient tissues and cancer cell-line models. DNAH9 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, DNAH9 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, DNAH9 RNA expression shows 15,925 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight MESO, KICH, and TGCT as cancer lineages where DNAH9 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 DNAH9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DNAH9 survival associations across molecular data types. DNAH9 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (10) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DNAH9 RNA expression–survival associations across cancer types. High DNAH9 expression shows unfavorable associations in LGG, but favorable associations in MESO, UCEC, UVM, ACC and BRCA. The MESO 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 MESO as the clearest survival context for DNAH9 RNA expression.
This table summarizes DNAH9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 2. The strongest signals are observed in KICH for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for DNAH9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DNAH9 shows lower tumor expression in KICH, LUAD, KIRC, LUSC, THCA and BRCA. The KICH box plot shows higher DNAH9 RNA expression in normal versus tumor tissue (log2 FC = −0.762, t-test p < 0.001).
This table shows molecular features associated with DNAH9 in patient tissues and cancer cell lines. In patient samples, DNAH9 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, DNAH9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BONE and LARGE_INTESTINE.
