Q-omics provides the consensus-scored PTPA profile across patient tissues and cancer cell-line models. PTPA expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PTPA is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, PTPA protein abundance shows 35,178 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, KIRC, and GBM as cancer lineages where PTPA 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 PTPA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PTPA survival associations across molecular data types. PTPA RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) and mass-spec protein abundance (12). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PTPA RNA expression–survival associations across cancer types. High PTPA expression shows unfavorable associations in ACC, LAML, UVM and MESO, but favorable associations in KIRP and UCEC. The ACC 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 ACC as the clearest survival context for PTPA RNA expression.
This table summarizes PTPA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 10. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for PTPA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PTPA shows lower tumor expression in KICH and higher tumor expression in KIRC, COAD, LIHC, HNSC and BRCA. The KIRC box plot shows higher PTPA RNA expression in tumor versus normal tissue (log2 FC = +0.429, t-test p < 0.001).
This table shows molecular features associated with PTPA in patient tissues and cancer cell lines. In patient samples, PTPA 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, PTPA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SKIN and LARGE_INTESTINE.
