IQ motif containing NGenealiases: KIAA1683 · SPGF78
Q-omics provides the consensus-scored IQCN profile across patient tissues and cancer cell-line models. IQCN expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, IQCN is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, IQCN RNA expression shows 18,347 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight HNSC, COAD, and THYM as cancer lineages where IQCN 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 IQCN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IQCN survival associations across molecular data types. IQCN RNA expression shows survival associations in the most cancer types (23), followed by mutation status (8) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IQCN RNA expression–survival associations across cancer types. High IQCN expression shows unfavorable associations in ACC, but favorable associations in HNSC, PAAD, BRCA, SKCM and UCS. 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 IQCN RNA expression.
This table summarizes IQCN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 1. The strongest signals are observed in COAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for IQCN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IQCN shows lower tumor expression in COAD, KIRC, HNSC, THCA, LUAD and LUSC. The COAD box plot shows higher IQCN RNA expression in normal versus tumor tissue (log2 FC = −1.239, t-test p < 0.001).
This table shows molecular features associated with IQCN in patient tissues and cancer cell lines. In patient samples, IQCN shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, IQCN 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 LUNG_NSCLC_LUAD and BLOOD_Leukemia.
