Q-omics provides the consensus-scored SERBP1P1 profile across patient tissues and cancer cell-line models. SERBP1P1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, SERBP1P1 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, SERBP1P1 RNA expression shows 16,248 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LGG, HNSC, and ACC as cancer lineages where SERBP1P1 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 SERBP1P1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERBP1P1 survival associations across molecular data types. SERBP1P1 RNA expression shows survival associations in the most cancer types (23). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERBP1P1 RNA expression–survival associations across cancer types. High SERBP1P1 expression shows unfavorable associations in LGG, MESO, BLCA, SARC and LUSC, but favorable associations in READ. The LGG 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 LGG as the clearest survival context for SERBP1P1 RNA expression.
This table summarizes SERBP1P1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for SERBP1P1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERBP1P1 shows higher tumor expression in HNSC, COAD, LUSC, KIRP, LIHC and LUAD. The HNSC box plot shows higher SERBP1P1 RNA expression in tumor versus normal tissue (log2 FC = +0.426, t-test p < 0.001).
This table shows molecular features associated with SERBP1P1 in patient tissues and cancer cell lines. In patient samples, SERBP1P1 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set.
