Q-omics provides the consensus-scored ITGB1BP2 profile across patient tissues and cancer cell-line models. ITGB1BP2 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, ITGB1BP2 is differentially expressed in 15, with the highest sampling consensus in KICH. Additionally, ITGB1BP2 RNA expression shows 17,760 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight COAD, KICH, and UVM as cancer lineages where ITGB1BP2 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 ITGB1BP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ITGB1BP2 survival associations across molecular data types. ITGB1BP2 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ITGB1BP2 RNA expression–survival associations across cancer types. High ITGB1BP2 expression shows unfavorable associations in COAD, ACC, UVM, KIRP and KIRC, but favorable associations in SKCM. The COAD 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 COAD as the clearest survival context for ITGB1BP2 RNA expression.
This table summarizes ITGB1BP2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 1. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ITGB1BP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ITGB1BP2 shows lower tumor expression in KICH, BLCA, UCEC and STAD and higher tumor expression in LIHC and THCA. The KICH box plot shows higher ITGB1BP2 RNA expression in normal versus tumor tissue (log2 FC = −0.526, t-test p < 0.001).
This table shows molecular features associated with ITGB1BP2 in patient tissues and cancer cell lines. In patient samples, ITGB1BP2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, ITGB1BP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
