Q-omics provides the consensus-scored OOSP4B profile across patient tissues and cancer cell-line models. OOSP4B expression is associated with patient survival in 13 of 34 cancer types, with the highest sampling consensus in KIRP. Additionally, OOSP4B RNA expression shows 5,671 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight KIRP, and STAD as cancer lineages where OOSP4B 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 OOSP4B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OOSP4B survival associations across molecular data types. OOSP4B RNA expression shows survival associations in the most cancer types (13). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OOSP4B RNA expression–survival associations across cancer types. High OOSP4B expression shows unfavorable associations in KIRP, UCEC, LIHC, KIRC and COAD, but favorable associations in ESCA. The KIRP 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 KIRP as the clearest survival context for OOSP4B RNA expression.
This table shows molecular features associated with OOSP4B in patient tissues and cancer cell lines. In patient samples, OOSP4B shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set. In cancer cell lines, OOSP4B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD.
