Q-omics provides the consensus-scored GPBP1L1 profile across patient tissues and cancer cell-line models. GPBP1L1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, GPBP1L1 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, GPBP1L1 RNA expression shows 20,960 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, KICH, and ACC as cancer lineages where GPBP1L1 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 GPBP1L1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GPBP1L1 survival associations across molecular data types. GPBP1L1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) 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 GPBP1L1 RNA expression–survival associations across cancer types. High GPBP1L1 expression shows unfavorable associations in LIHC, LGG, ACC, PAAD and CESC, but favorable associations in KIRC. The LIHC 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 LIHC as the clearest survival context for GPBP1L1 RNA expression.
This table summarizes GPBP1L1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 4. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for GPBP1L1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GPBP1L1 shows lower tumor expression in KICH, COAD, KIRC and READ and higher tumor expression in HNSC and BLCA. The KICH box plot shows higher GPBP1L1 RNA expression in normal versus tumor tissue (log2 FC = −1.153, t-test p < 0.001).
This table shows molecular features associated with GPBP1L1 in patient tissues and cancer cell lines. In patient samples, GPBP1L1 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, GPBP1L1 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_Leukemia and BLOOD_Lymphoma.
