Q-omics provides the consensus-scored IGKV1-16 profile across patient tissues and cancer cell-line models. IGKV1-16 expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, IGKV1-16 is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, IGKV1-16 RNA expression shows 15,260 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, COAD, and LSCC as cancer lineages where IGKV1-16 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 IGKV1-16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGKV1-16 survival associations across molecular data types. IGKV1-16 RNA expression shows survival associations in the most cancer types (29), followed by mutation status (1) 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 IGKV1-16 RNA expression–survival associations across cancer types. High IGKV1-16 expression shows unfavorable associations in UVM, but favorable associations in HNSC, SKCM, SARC, BRCA and ACC. 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 IGKV1-16 RNA expression.
This table summarizes IGKV1-16 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 5. The strongest signals are observed in COAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for IGKV1-16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGKV1-16 shows lower tumor expression in COAD, LIHC and BRCA and higher tumor expression in LUAD, KIRC and ESCA. The COAD box plot shows higher IGKV1-16 RNA expression in normal versus tumor tissue (log2 FC = −3.172, t-test p < 0.001).
This table shows molecular features associated with IGKV1-16 in patient tissues and cancer cell lines. In patient samples, IGKV1-16 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set.
