Q-omics provides the consensus-scored JAML profile across patient tissues and cancer cell-line models. JAML expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, JAML is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, JAML RNA expression shows 24,972 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, KIRC, and LSCC as cancer lineages where JAML 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 JAML — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes JAML survival associations across molecular data types. JAML RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible JAML RNA expression–survival associations across cancer types. High JAML expression shows unfavorable associations in UVM, but favorable associations in HNSC, SKCM, UCEC, CESC and LUAD. The HNSC 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 HNSC as the clearest survival context for JAML RNA expression.
This table summarizes JAML 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 2. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for JAML. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. JAML shows lower tumor expression in COAD, LUAD, LUSC and UCEC and higher tumor expression in KIRC and THCA. The KIRC box plot shows higher JAML RNA expression in tumor versus normal tissue (log2 FC = +1.634, t-test p < 0.001).
This table shows molecular features associated with JAML in patient tissues and cancer cell lines. In patient samples, JAML shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, JAML RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and LIVER.
