speedy/RINGO cell cycle regulator family member AGenealiases: RINGO3 · RINGOA · SPDY1 · SPY1
Q-omics provides the consensus-scored SPDYA profile across patient tissues and cancer cell-line models. SPDYA expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SPDYA is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, SPDYA RNA expression shows 19,296 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight ACC, HNSC, and UVM as cancer lineages where SPDYA 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 SPDYA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SPDYA survival associations across molecular data types. SPDYA RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SPDYA RNA expression–survival associations across cancer types. High SPDYA expression shows unfavorable associations in ACC, KIRC, LIHC and MESO, but favorable associations in UCS and BLCA. 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 SPDYA RNA expression.
This table summarizes SPDYA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for SPDYA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SPDYA shows higher tumor expression in HNSC, BLCA, LIHC, KIRC, COAD and UCEC. The HNSC box plot shows higher SPDYA RNA expression in tumor versus normal tissue (log2 FC = +0.238, t-test p < 0.001).
This table shows molecular features associated with SPDYA in patient tissues and cancer cell lines. In patient samples, SPDYA 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, SPDYA 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 BLOOD_Myeloma and BLOOD_Leukemia.
