Q-omics provides the consensus-scored SPATA31D3 profile across patient tissues and cancer cell-line models. SPATA31D3 expression is associated with patient survival in 10 of 34 cancer types, with the highest sampling consensus in KICH. Additionally, SPATA31D3 RNA expression shows 5,898 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight KICH, and STAD as cancer lineages where SPATA31D3 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 SPATA31D3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SPATA31D3 survival associations across molecular data types. SPATA31D3 RNA expression shows survival associations in the most cancer types (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SPATA31D3 RNA expression–survival associations across cancer types. High SPATA31D3 expression shows unfavorable associations in KICH, LUAD, KIRC, LUSC, THCA and LGG. The KICH 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 KICH as the clearest survival context for SPATA31D3 RNA expression.
This table shows molecular features associated with SPATA31D3 in patient tissues and cancer cell lines. In patient samples, SPATA31D3 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, SPATA31D3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUSC, while CRISPR and shRNA rows add functional-dependency signals in STOMACH.
