Q-omics provides the consensus-scored SF3B2 profile across patient tissues and cancer cell-line models. SF3B2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SF3B2 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, SF3B2 protein abundance shows 31,364 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where SF3B2 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 SF3B2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SF3B2 survival associations across molecular data types. SF3B2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (8) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SF3B2 RNA expression–survival associations across cancer types. High SF3B2 expression shows unfavorable associations in ACC, KICH, LIHC, BLCA and MESO, but favorable associations in KIRC. The ACC 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 ACC as the clearest survival context for SF3B2 RNA expression.
This table summarizes SF3B2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SF3B2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SF3B2 shows higher tumor expression in HNSC, BLCA, LIHC, STAD, LUSC and COAD. The HNSC box plot shows higher SF3B2 RNA expression in tumor versus normal tissue (log2 FC = +0.939, t-test p < 0.001).
This table shows molecular features associated with SF3B2 in patient tissues and cancer cell lines. In patient samples, SF3B2 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, SF3B2 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 BLOOD_Leukemia and LARGE_INTESTINE.
