SET binding factor 2Genealiases: CMT4B2 · DENND7B · MTMR13
Q-omics provides the consensus-scored SBF2 profile across patient tissues and cancer cell-line models. SBF2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SBF2 is differentially expressed in 7, with the highest sampling consensus in LIHC. Additionally, SBF2 protein abundance shows 33,775 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KIRC, LIHC, and HNSC as cancer lineages where SBF2 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 SBF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SBF2 survival associations across molecular data types. SBF2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (13) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SBF2 RNA expression–survival associations across cancer types. High SBF2 expression shows unfavorable associations in ACC, BLCA, MESO and LUAD, but favorable associations in KIRC and UCS. The KIRC 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 KIRC as the clearest survival context for SBF2 RNA expression.
This table summarizes SBF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 12. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SBF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SBF2 shows lower tumor expression in UCEC and BRCA and higher tumor expression in LIHC, CHOL, PAAD and HNSC. The LIHC box plot shows higher SBF2 RNA expression in tumor versus normal tissue (log2 FC = +0.657, t-test p < 0.001).
This table shows molecular features associated with SBF2 in patient tissues and cancer cell lines. In patient samples, SBF2 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, SBF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
