Q-omics provides the consensus-scored PATJ profile across patient tissues and cancer cell-line models. PATJ expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PATJ is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, PATJ RNA expression shows 19,906 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRC, KICH, and THYM as cancer lineages where PATJ 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 PATJ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PATJ survival associations across molecular data types. PATJ RNA expression shows survival associations in the most cancer types (23), followed by mutation status (9) 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 PATJ RNA expression–survival associations across cancer types. High PATJ expression shows unfavorable associations in PAAD, CESC and LIHC, but favorable associations in KIRC, MESO and STAD. 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 PATJ RNA expression.
This table summarizes PATJ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PATJ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PATJ shows lower tumor expression in KICH, KIRP and KIRC and higher tumor expression in BRCA, CHOL and LIHC. The KICH box plot shows higher PATJ RNA expression in normal versus tumor tissue (log2 FC = −1.586, t-test p < 0.001).
This table shows molecular features associated with PATJ in patient tissues and cancer cell lines. In patient samples, PATJ shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, PATJ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and OVARY.
