Q-omics provides the consensus-scored SAP130 profile across patient tissues and cancer cell-line models. SAP130 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SAP130 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, SAP130 protein abundance shows 32,267 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where SAP130 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 SAP130 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SAP130 survival associations across molecular data types. SAP130 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (6) and mass-spec protein abundance (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SAP130 RNA expression–survival associations across cancer types. High SAP130 expression shows unfavorable associations in ACC, MESO, LIHC and KICH, but favorable associations in KIRC and BRCA. 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 SAP130 RNA expression.
This table summarizes SAP130 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 9. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SAP130. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SAP130 shows lower tumor expression in THCA and higher tumor expression in HNSC, KIRP, LIHC, LUSC and BLCA. The HNSC box plot shows higher SAP130 RNA expression in tumor versus normal tissue (log2 FC = +0.920, t-test p < 0.001).
This table shows molecular features associated with SAP130 in patient tissues and cancer cell lines. In patient samples, SAP130 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, SAP130 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Leukemia.
