Q-omics provides the consensus-scored VNN1 profile across patient tissues and cancer cell-line models. VNN1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in DLBC. Among the 18 cancer types available for tumor–normal comparison, VNN1 is differentially expressed in 8, with the highest sampling consensus in KICH. Additionally, VNN1 protein abundance shows 23,275 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight DLBC, KICH, and LSCC as cancer lineages where VNN1 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 VNN1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes VNN1 survival associations across molecular data types. VNN1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (6) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible VNN1 RNA expression–survival associations across cancer types. High VNN1 expression shows unfavorable associations in OV and LGG, but favorable associations in DLBC, HNSC, SKCM and UCEC. The DLBC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify DLBC as the clearest survival context for VNN1 RNA expression.
This table summarizes VNN1 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 8. The strongest signals are observed in KICH for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for VNN1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. VNN1 shows lower tumor expression in KICH, LIHC, KIRP, CHOL and BRCA and higher tumor expression in COAD. The KICH box plot shows higher VNN1 RNA expression in normal versus tumor tissue (log2 FC = −3.120, t-test p < 0.001).
This table shows molecular features associated with VNN1 in patient tissues and cancer cell lines. In patient samples, VNN1 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, VNN1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LIVER and SOFT_TISSUE.
