Q-omics provides the consensus-scored SERF1A profile across patient tissues and cancer cell-line models. SERF1A expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LUSC. Among the 18 cancer types available for tumor–normal comparison, SERF1A is differentially expressed in 5, with the highest sampling consensus in KICH. Additionally, SERF1A RNA expression shows 10,616 significant gene co-expression associations, with the highest sampling consensus in KICH. Together, these results highlight LUSC, and KICH as cancer lineages where SERF1A 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 SERF1A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERF1A survival associations across molecular data types. SERF1A RNA expression shows survival associations in the most cancer types (22). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERF1A RNA expression–survival associations across cancer types. High SERF1A expression shows unfavorable associations in KICH, LIHC and THCA, but favorable associations in LUSC, DLBC and BRCA. The LUSC 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 LUSC as the clearest survival context for SERF1A RNA expression.
This table summarizes SERF1A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for SERF1A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERF1A shows lower tumor expression in KICH and COAD and higher tumor expression in LIHC, LUAD and CHOL. The KICH box plot shows higher SERF1A RNA expression in normal versus tumor tissue (log2 FC = −0.175, t-test p < 0.001).
This table shows molecular features associated with SERF1A in patient tissues and cancer cell lines. In patient samples, SERF1A shows the broadest associations at the RNA and protein expression levels, with KICH recurring as the lineage with the largest associated feature set. In cancer cell lines, SERF1A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT.
