Q-omics provides the consensus-scored SERF1B profile across patient tissues and cancer cell-line models. SERF1B expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, SERF1B is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, SERF1B RNA expression shows 19,486 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LUAD, KICH, and ACC as cancer lineages where SERF1B 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 SERF1B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERF1B survival associations across molecular data types. SERF1B RNA expression shows survival associations in the most cancer types (26). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERF1B RNA expression–survival associations across cancer types. High SERF1B expression shows unfavorable associations in KIRP, but favorable associations in LUAD, UCS, KIRC, PAAD and LAML. The LUAD 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 LUAD as the clearest survival context for SERF1B RNA expression.
This table summarizes SERF1B 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 KICH for RNA.
This table ranks reproducible tumor–normal expression differences for SERF1B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERF1B shows lower tumor expression in KICH, THCA, COAD and KIRC and higher tumor expression in LIHC and CHOL. The KICH box plot shows higher SERF1B RNA expression in normal versus tumor tissue (log2 FC = −0.669, t-test p < 0.001).
This table shows molecular features associated with SERF1B in patient tissues and cancer cell lines. In patient samples, SERF1B 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, SERF1B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia.
