Q-omics provides the consensus-scored APTX profile across patient tissues and cancer cell-line models. APTX expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, APTX is differentially expressed in 18, with the highest sampling consensus in COAD. Additionally, APTX protein abundance shows 20,498 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KICH, COAD, and PDAC as cancer lineages where APTX 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 APTX — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APTX survival associations across molecular data types. APTX RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) 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 APTX RNA expression–survival associations across cancer types. High APTX expression shows unfavorable associations in KICH, UVM, SKCM, LIHC and ACC, but favorable associations in KIRC. The KICH 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 KICH as the clearest survival context for APTX RNA expression.
This table summarizes APTX tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 18, while mass-spec protein shows differences in 8. The strongest signals are observed in BLCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for APTX. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APTX shows higher tumor expression in COAD, BLCA, LIHC, HNSC, STAD and UCEC. The COAD box plot shows higher APTX RNA expression in tumor versus normal tissue (log2 FC = +0.746, t-test p < 0.001).
This table shows molecular features associated with APTX in patient tissues and cancer cell lines. In patient samples, APTX shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, APTX RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in OVARY and UPPER_AERODIGESTIVE_TRACT.
