Q-omics provides the consensus-scored SPATA31D4 profile across patient tissues and cancer cell-line models. SPATA31D4 expression is associated with patient survival in 6 of 34 cancer types, with the highest sampling consensus in OV. Additionally, SPATA31D4 RNA expression shows 3,281 significant gene co-expression associations, with the highest sampling consensus in ESCA. Together, these results highlight OV, and ESCA as cancer lineages where SPATA31D4 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 SPATA31D4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SPATA31D4 survival associations across molecular data types. SPATA31D4 RNA expression shows survival associations in the most cancer types (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SPATA31D4 RNA expression–survival associations across cancer types. High SPATA31D4 expression shows unfavorable associations in OV, CESC, KIRC, BLCA, STAD and BRCA. The OV 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 OV as the clearest survival context for SPATA31D4 RNA expression.
This table shows molecular features associated with SPATA31D4 in patient tissues and cancer cell lines. In patient samples, SPATA31D4 shows the broadest associations at the RNA and protein expression levels, with ESCA recurring as the lineage with the largest associated feature set. In cancer cell lines, SPATA31D4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and LUNG_NSCLC_LUAD.
