Q-omics provides the consensus-scored CGNL1 profile across patient tissues and cancer cell-line models. CGNL1 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, CGNL1 is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, CGNL1 protein abundance shows 22,460 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, KIRC, and LSCC as cancer lineages where CGNL1 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 CGNL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CGNL1 survival associations across molecular data types. CGNL1 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (9) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CGNL1 RNA expression–survival associations across cancer types. High CGNL1 expression shows unfavorable associations in BLCA and LGG, but favorable associations in HNSC, KIRC, LIHC and UCEC. 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 CGNL1 RNA expression.
This table summarizes CGNL1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CGNL1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CGNL1 shows lower tumor expression in KIRC, HNSC, KIRP, BLCA, THCA and LUAD. The KIRC box plot shows higher CGNL1 RNA expression in normal versus tumor tissue (log2 FC = −2.558, t-test p < 0.001).
This table shows molecular features associated with CGNL1 in patient tissues and cancer cell lines. In patient samples, CGNL1 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, CGNL1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and LUNG_NSCLC_LUAD.
