Q-omics provides the consensus-scored SQOR profile across patient tissues and cancer cell-line models. SQOR expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, SQOR is differentially expressed in 11, with the highest sampling consensus in COAD. Additionally, SQOR protein abundance shows 26,942 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, COAD, and GBM as cancer lineages where SQOR 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 SQOR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SQOR survival associations across molecular data types. SQOR RNA expression shows survival associations in the most cancer types (24), followed by mutation status (6) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SQOR RNA expression–survival associations across cancer types. High SQOR expression shows unfavorable associations in UVM, LUAD and LGG, but favorable associations in MESO, SKCM and KIRC. The UVM 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 UVM as the clearest survival context for SQOR RNA expression.
This table summarizes SQOR tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 8. The strongest signals are observed in KIRC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SQOR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SQOR shows lower tumor expression in COAD and LUSC and higher tumor expression in KIRC, KIRP, LIHC and BRCA. The COAD box plot shows higher SQOR RNA expression in normal versus tumor tissue (log2 FC = −1.628, t-test p < 0.001).
This table shows molecular features associated with SQOR in patient tissues and cancer cell lines. In patient samples, SQOR 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, SQOR RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Lymphoma.
