amyloid beta precursor protein binding family B member 1 interacting proteinGenealiases: INAG1 · PREL1 · RARP1 · RIAM
Q-omics provides the consensus-scored APBB1IP profile across patient tissues and cancer cell-line models. APBB1IP expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, APBB1IP is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, APBB1IP protein abundance shows 26,138 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where APBB1IP 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 APBB1IP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APBB1IP survival associations across molecular data types. APBB1IP RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APBB1IP RNA expression–survival associations across cancer types. High APBB1IP expression shows unfavorable associations in LGG, but favorable associations in SKCM, HNSC, UCEC, CESC and KIRP. The SKCM 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 SKCM as the clearest survival context for APBB1IP RNA expression.
This table summarizes APBB1IP 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 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for APBB1IP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APBB1IP shows lower tumor expression in COAD, LUSC and BRCA and higher tumor expression in KIRC, KIRP and STAD. The KIRC box plot shows higher APBB1IP RNA expression in tumor versus normal tissue (log2 FC = +3.205, t-test p < 0.001).
This table shows molecular features associated with APBB1IP in patient tissues and cancer cell lines. In patient samples, APBB1IP 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, APBB1IP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Lymphoma.
