SET and mariner transposase domain methyltransferaseGenealiases: METNASE · Mar1
Q-omics provides the consensus-scored SETMAR profile across patient tissues and cancer cell-line models. SETMAR expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, SETMAR is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, SETMAR protein abundance shows 26,544 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, KIRC, and GBM as cancer lineages where SETMAR 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 SETMAR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SETMAR survival associations across molecular data types. SETMAR RNA expression shows survival associations in the most cancer types (21), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SETMAR RNA expression–survival associations across cancer types. High SETMAR expression shows unfavorable associations in LIHC, ACC and SKCM, but favorable associations in DLBC, READ and UVM. The LIHC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify LIHC as the clearest survival context for SETMAR RNA expression.
This table summarizes SETMAR tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SETMAR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SETMAR shows lower tumor expression in KIRC, HNSC, THCA, KIRP and UCEC and higher tumor expression in LIHC. The KIRC box plot shows higher SETMAR RNA expression in normal versus tumor tissue (log2 FC = −1.052, t-test p < 0.001).
This table shows molecular features associated with SETMAR in patient tissues and cancer cell lines. In patient samples, SETMAR 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, SETMAR 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 LUNG_SCLC and BLOOD_Leukemia.
