speedy/RINGO cell cycle regulator family member CGenealiases: RINGOC · Ringo2
Q-omics provides the consensus-scored SPDYC profile across patient tissues and cancer cell-line models. SPDYC expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in BRCA. Among the 18 cancer types available for tumor–normal comparison, SPDYC is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, SPDYC RNA expression shows 11,452 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight BRCA, KIRC, and TGCT as cancer lineages where SPDYC 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 SPDYC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SPDYC survival associations across molecular data types. SPDYC RNA expression shows survival associations in the most cancer types (21), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SPDYC RNA expression–survival associations across cancer types. High SPDYC expression shows unfavorable associations in BRCA, ACC and KIRC, but favorable associations in LIHC, THCA and UCEC. The BRCA 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 BRCA as the clearest survival context for SPDYC RNA expression.
This table summarizes SPDYC tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for SPDYC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SPDYC shows lower tumor expression in KIRC and THCA and higher tumor expression in COAD, STAD, HNSC and BRCA. The KIRC box plot shows higher SPDYC RNA expression in normal versus tumor tissue (log2 FC = −0.352, t-test p < 0.001).
This table shows molecular features associated with SPDYC in patient tissues and cancer cell lines. In patient samples, SPDYC 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, SPDYC RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
