Q-omics provides the consensus-scored TSPY3 profile across patient tissues and cancer cell-line models. TSPY3 expression is associated with patient survival in 8 of 34 cancer types, with the highest sampling consensus in LIHC. Additionally, TSPY3 RNA expression shows 5,394 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight LIHC, and TGCT as cancer lineages where TSPY3 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 TSPY3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TSPY3 survival associations across molecular data types. TSPY3 RNA expression shows survival associations in the most cancer types (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TSPY3 RNA expression–survival associations across cancer types. High TSPY3 expression shows unfavorable associations in LIHC, KIRP, LUAD, LGG, SARC and STAD. The LIHC 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 LIHC as the clearest survival context for TSPY3 RNA expression.
This table shows molecular features associated with TSPY3 in patient tissues and cancer cell lines. In patient samples, TSPY3 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, TSPY3 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_Lymphoma.
