Q-omics provides the consensus-scored SAFB2 profile across patient tissues and cancer cell-line models. SAFB2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, SAFB2 is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, SAFB2 protein abundance shows 26,718 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KICH, LIHC, and HNSC as cancer lineages where SAFB2 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 SAFB2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SAFB2 survival associations across molecular data types. SAFB2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SAFB2 RNA expression–survival associations across cancer types. High SAFB2 expression shows unfavorable associations in KICH, ACC, LIHC and LGG, but favorable associations in PAAD and BLCA. The KICH Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify KICH as the clearest survival context for SAFB2 RNA expression.
This table summarizes SAFB2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 7. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SAFB2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SAFB2 shows lower tumor expression in THCA and higher tumor expression in LIHC, HNSC, STAD, COAD and CHOL. The LIHC box plot shows higher SAFB2 RNA expression in tumor versus normal tissue (log2 FC = +1.179, t-test p < 0.001).
This table shows molecular features associated with SAFB2 in patient tissues and cancer cell lines. In patient samples, SAFB2 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, SAFB2 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 LARGE_INTESTINE and BLOOD_Lymphoma.
