Q-omics provides the consensus-scored IGLV1-50 profile across patient tissues and cancer cell-line models. IGLV1-50 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, IGLV1-50 is differentially expressed in 8, with the highest sampling consensus in COAD. Additionally, IGLV1-50 RNA expression shows 9,600 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight HNSC, COAD, and TGCT as cancer lineages where IGLV1-50 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 IGLV1-50 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGLV1-50 survival associations across molecular data types. IGLV1-50 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (2) 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 IGLV1-50 RNA expression–survival associations across cancer types. High IGLV1-50 expression shows unfavorable associations in KIRP, but favorable associations in HNSC, COAD, SKCM, BLCA and BRCA. 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 IGLV1-50 RNA expression.
This table summarizes IGLV1-50 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 2. The strongest signals are observed in COAD for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for IGLV1-50. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGLV1-50 shows lower tumor expression in COAD, BRCA, KICH, LIHC and READ and higher tumor expression in LUAD. The COAD box plot shows higher IGLV1-50 RNA expression in normal versus tumor tissue (log2 FC = −3.120, t-test p < 0.001).
This table shows molecular features associated with IGLV1-50 in patient tissues and cancer cell lines. In patient samples, IGLV1-50 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set.
