Q-omics provides the consensus-scored SERBP1 profile across patient tissues and cancer cell-line models. SERBP1 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SERBP1 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, SERBP1 protein abundance shows 30,038 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where SERBP1 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 SERBP1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERBP1 survival associations across molecular data types. SERBP1 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERBP1 RNA expression–survival associations across cancer types. High SERBP1 expression shows unfavorable associations in ACC, KIRP, LIHC, MESO, LGG and LUAD. The ACC 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 ACC as the clearest survival context for SERBP1 RNA expression.
This table summarizes SERBP1 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 HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SERBP1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERBP1 shows lower tumor expression in KICH and higher tumor expression in HNSC, LIHC, STAD, LUSC and LUAD. The HNSC box plot shows higher SERBP1 RNA expression in tumor versus normal tissue (log2 FC = +0.513, t-test p < 0.001).
This table shows molecular features associated with SERBP1 in patient tissues and cancer cell lines. In patient samples, SERBP1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, SERBP1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Lymphoma.
