Q-omics provides the consensus-scored JRKL profile across patient tissues and cancer cell-line models. JRKL expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in BRCA. Among the 18 cancer types available for tumor–normal comparison, JRKL is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, JRKL RNA expression shows 21,041 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight BRCA, HNSC, and ACC as cancer lineages where JRKL 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 JRKL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes JRKL survival associations across molecular data types. JRKL RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible JRKL RNA expression–survival associations across cancer types. High JRKL expression shows unfavorable associations in BRCA, ACC, KIRP, CESC and HNSC, but favorable associations in KIRC. The BRCA 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 BRCA as the clearest survival context for JRKL RNA expression.
This table summarizes JRKL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 2. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for JRKL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. JRKL shows lower tumor expression in THCA and higher tumor expression in HNSC, KIRC, STAD, LUAD and LIHC. The HNSC box plot shows higher JRKL RNA expression in tumor versus normal tissue (log2 FC = +0.975, t-test p < 0.001).
This table shows molecular features associated with JRKL in patient tissues and cancer cell lines. In patient samples, JRKL 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, JRKL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BREAST.
