Q-omics provides the consensus-scored APH1A profile across patient tissues and cancer cell-line models. APH1A expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, APH1A is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, APH1A RNA expression shows 18,771 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, HNSC, and ACC as cancer lineages where APH1A 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 APH1A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APH1A survival associations across molecular data types. APH1A RNA expression shows survival associations in the most cancer types (28), followed by mutation status (2) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APH1A RNA expression–survival associations across cancer types. High APH1A expression shows unfavorable associations in UVM, ACC, LIHC, KIRP and CESC, but favorable associations in KIRC. The UVM 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 UVM as the clearest survival context for APH1A RNA expression.
This table summarizes APH1A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for APH1A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APH1A shows lower tumor expression in KICH and higher tumor expression in HNSC, BLCA, LIHC, BRCA and STAD. The HNSC box plot shows higher APH1A RNA expression in tumor versus normal tissue (log2 FC = +0.746, t-test p < 0.001).
This table shows molecular features associated with APH1A in patient tissues and cancer cell lines. In patient samples, APH1A shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, APH1A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and UPPER_AERODIGESTIVE_TRACT.
