Q-omics provides the consensus-scored DDHD1 profile across patient tissues and cancer cell-line models. DDHD1 expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, DDHD1 is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, DDHD1 protein abundance shows 22,771 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KICH, HNSC, and GBM as cancer lineages where DDHD1 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 DDHD1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DDHD1 survival associations across molecular data types. DDHD1 RNA expression shows survival associations in the most cancer types (29), followed by mutation status (5) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DDHD1 RNA expression–survival associations across cancer types. High DDHD1 expression shows unfavorable associations in KICH, LIHC and MESO, but favorable associations in SKCM, GBM and PAAD. The KICH 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 KICH as the clearest survival context for DDHD1 RNA expression.
This table summarizes DDHD1 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 8. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for DDHD1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DDHD1 shows lower tumor expression in LUAD and higher tumor expression in HNSC, KIRC, KIRP, LIHC and STAD. The HNSC box plot shows higher DDHD1 RNA expression in tumor versus normal tissue (log2 FC = +1.079, t-test p < 0.001).
This table shows molecular features associated with DDHD1 in patient tissues and cancer cell lines. In patient samples, DDHD1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, DDHD1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and LARGE_INTESTINE.
