Q-omics provides the consensus-scored SAYSD1 profile across patient tissues and cancer cell-line models. SAYSD1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, SAYSD1 is differentially expressed in 14, with the highest sampling consensus in LIHC. Additionally, SAYSD1 protein abundance shows 21,702 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight BLCA, LIHC, and PDAC as cancer lineages where SAYSD1 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 SAYSD1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SAYSD1 survival associations across molecular data types. SAYSD1 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (2) 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 SAYSD1 RNA expression–survival associations across cancer types. High SAYSD1 expression shows unfavorable associations in LIHC, but favorable associations in BLCA, MESO, READ, OV and LUSC. The BLCA Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify BLCA as the clearest survival context for SAYSD1 RNA expression.
This table summarizes SAYSD1 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 7. The strongest signals are observed in LIHC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SAYSD1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SAYSD1 shows lower tumor expression in KICH and THCA and higher tumor expression in LIHC, HNSC, BRCA and STAD. The LIHC box plot shows higher SAYSD1 RNA expression in tumor versus normal tissue (log2 FC = +1.197, t-test p < 0.001).
This table shows molecular features associated with SAYSD1 in patient tissues and cancer cell lines. In patient samples, SAYSD1 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, SAYSD1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
