Q-omics provides the consensus-scored SERTAD3 profile across patient tissues and cancer cell-line models. SERTAD3 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in PAAD. Among the 18 cancer types available for tumor–normal comparison, SERTAD3 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, SERTAD3 RNA expression shows 18,232 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight PAAD, HNSC, and UVM as cancer lineages where SERTAD3 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 SERTAD3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERTAD3 survival associations across molecular data types. SERTAD3 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERTAD3 RNA expression–survival associations across cancer types. High SERTAD3 expression shows unfavorable associations in PAAD, LGG, UVM, ACC and KIRC, but favorable associations in BRCA. The PAAD 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 PAAD as the clearest survival context for SERTAD3 RNA expression.
This table summarizes SERTAD3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for SERTAD3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERTAD3 shows lower tumor expression in KICH and UCEC and higher tumor expression in HNSC, LIHC, BRCA and CHOL. The HNSC box plot shows higher SERTAD3 RNA expression in tumor versus normal tissue (log2 FC = +0.526, t-test p < 0.001).
This table shows molecular features associated with SERTAD3 in patient tissues and cancer cell lines. In patient samples, SERTAD3 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, SERTAD3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
