Q-omics provides the consensus-scored SACS profile across patient tissues and cancer cell-line models. SACS expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SACS is differentially expressed in 13, with the highest sampling consensus in KIRP. Additionally, SACS RNA expression shows 21,406 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight ACC, KIRP, and UVM as cancer lineages where SACS 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 SACS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SACS survival associations across molecular data types. SACS RNA expression shows survival associations in the most cancer types (29), followed by mutation status (11) 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 SACS RNA expression–survival associations across cancer types. High SACS expression shows unfavorable associations in ACC, MESO, UVM, OV, BLCA and UCEC. 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 SACS RNA expression.
This table summarizes SACS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SACS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SACS shows lower tumor expression in THCA and BRCA and higher tumor expression in KIRP, KIRC, HNSC and LIHC. The KIRP box plot shows higher SACS RNA expression in tumor versus normal tissue (log2 FC = +1.664, t-test p < 0.001).
This table shows molecular features associated with SACS in patient tissues and cancer cell lines. In patient samples, SACS shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, SACS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and LARGE_INTESTINE.
