Q-omics provides the consensus-scored ANP32D profile across patient tissues and cancer cell-line models. ANP32D expression is associated with patient survival in 18 of 34 cancer types, with the highest sampling consensus in DLBC. Among the 18 cancer types available for tumor–normal comparison, ANP32D is differentially expressed in 6, with the highest sampling consensus in HNSC. Additionally, ANP32D RNA expression shows 10,407 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight DLBC, HNSC, and GBM as cancer lineages where ANP32D 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 ANP32D — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ANP32D survival associations across molecular data types. ANP32D RNA expression shows survival associations in the most cancer types (18), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ANP32D RNA expression–survival associations across cancer types. High ANP32D expression shows unfavorable associations in DLBC, LIHC and CHOL, but favorable associations in BLCA, GBM and LUAD. The DLBC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify DLBC as the clearest survival context for ANP32D RNA expression.
This table summarizes ANP32D tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 6, while mass-spec protein shows differences in 1. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ANP32D. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ANP32D shows lower tumor expression in LUAD and higher tumor expression in HNSC, COAD, ESCA, STAD and KICH. The HNSC box plot shows higher ANP32D RNA expression in tumor versus normal tissue (log2 FC = +0.085, t-test p = .001).
This table shows molecular features associated with ANP32D in patient tissues and cancer cell lines. In patient samples, ANP32D 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, ANP32D RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUSC, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Lymphoma.
