Q-omics provides the consensus-scored IGLV9-49 profile across patient tissues and cancer cell-line models. IGLV9-49 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, IGLV9-49 is differentially expressed in 8, with the highest sampling consensus in COAD. Additionally, IGLV9-49 RNA expression shows 9,568 significant mutation-linked associations, with the highest sampling consensus in UCEC. Together, these results highlight HNSC, COAD, and UCEC as cancer lineages where IGLV9-49 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 IGLV9-49 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGLV9-49 survival associations across molecular data types. IGLV9-49 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3) 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 IGLV9-49 RNA expression–survival associations across cancer types. High IGLV9-49 expression shows unfavorable associations in KIRC, but favorable associations in HNSC, SKCM, LUAD, UCEC and COAD. 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 IGLV9-49 RNA expression.
This table summarizes IGLV9-49 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 5. The strongest signals are observed in COAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for IGLV9-49. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGLV9-49 shows lower tumor expression in COAD, BRCA and READ and higher tumor expression in KIRC, ESCA and LUAD. The COAD box plot shows higher IGLV9-49 RNA expression in normal versus tumor tissue (log2 FC = −2.726, t-test p < 0.001).
This table shows molecular features associated with IGLV9-49 in patient tissues and cancer cell lines. In patient samples, IGLV9-49 shows the broadest associations at the RNA and protein expression levels, with UCEC recurring as the lineage with the largest associated feature set. In cancer cell lines, IGLV9-49 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 SKIN.
