Q-omics provides the consensus-scored OC90 profile across patient tissues and cancer cell-line models. OC90 expression is associated with patient survival in 13 of 34 cancer types, with the highest sampling consensus in ACC. Additionally, OC90 RNA expression shows 5,996 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight ACC, and TGCT as cancer lineages where OC90 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 OC90 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OC90 survival associations across molecular data types. OC90 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 OC90 RNA expression–survival associations across cancer types. High OC90 expression shows unfavorable associations in ACC, UCEC, KIRC, CESC, THCA and READ. 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 OC90 RNA expression.
This table shows molecular features associated with OC90 in patient tissues and cancer cell lines. In patient samples, OC90 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, OC90 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and LARGE_INTESTINE.
