Q-omics provides the consensus-scored ADNP profile across patient tissues and cancer cell-line models. ADNP expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ADNP is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, ADNP protein abundance shows 29,652 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where ADNP 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 ADNP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ADNP survival associations across molecular data types. ADNP RNA expression shows survival associations in the most cancer types (29), followed by mutation status (7) 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 ADNP RNA expression–survival associations across cancer types. High ADNP expression shows unfavorable associations in LIHC, ACC and MESO, but favorable associations in KIRC, UCS and HNSC. 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 ADNP RNA expression.
This table summarizes ADNP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, 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 ADNP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ADNP shows higher tumor expression in HNSC, BLCA, COAD, LIHC, STAD and BRCA. The HNSC box plot shows higher ADNP RNA expression in tumor versus normal tissue (log2 FC = +0.826, t-test p < 0.001).
This table shows molecular features associated with ADNP in patient tissues and cancer cell lines. In patient samples, ADNP 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, ADNP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Leukemia.
