Q-omics provides the consensus-scored IBTK profile across patient tissues and cancer cell-line models. IBTK expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, IBTK is differentially expressed in 12, with the highest sampling consensus in STAD. Additionally, IBTK RNA expression shows 20,898 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight CESC, STAD, and ACC as cancer lineages where IBTK 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 IBTK — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IBTK survival associations across molecular data types. IBTK RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) 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 IBTK RNA expression–survival associations across cancer types. High IBTK expression shows unfavorable associations in CESC, LUSC and KICH, but favorable associations in KIRC, SKCM and UCS. The CESC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify CESC as the clearest survival context for IBTK RNA expression.
This table summarizes IBTK 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 3. The strongest signals are observed in STAD for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for IBTK. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IBTK shows lower tumor expression in KICH and THCA and higher tumor expression in STAD, KIRP, LUAD and BRCA. The STAD box plot shows higher IBTK RNA expression in tumor versus normal tissue (log2 FC = +0.668, t-test p = .001).
This table shows molecular features associated with IBTK in patient tissues and cancer cell lines. In patient samples, IBTK shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, IBTK 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 BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
