Q-omics provides the consensus-scored SAFB profile across patient tissues and cancer cell-line models. SAFB 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, SAFB is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, SAFB protein abundance shows 31,246 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where SAFB 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 SAFB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SAFB survival associations across molecular data types. SAFB RNA expression shows survival associations in the most cancer types (22), followed by mutation status (10) 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 SAFB RNA expression–survival associations across cancer types. High SAFB expression shows unfavorable associations in ACC, KIRP and LIHC, but favorable associations in UCEC, PAAD and THYM. 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 SAFB RNA expression.
This table summarizes SAFB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SAFB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SAFB shows higher tumor expression in HNSC, KIRC, COAD, STAD, LIHC and BLCA. The HNSC box plot shows higher SAFB RNA expression in tumor versus normal tissue (log2 FC = +1.002, t-test p < 0.001).
This table shows molecular features associated with SAFB in patient tissues and cancer cell lines. In patient samples, SAFB 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, SAFB 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_Lymphoma and BLOOD_Leukemia.
