Q-omics provides the consensus-scored TSPY4 profile across patient tissues and cancer cell-line models. TSPY4 expression is associated with patient survival in 4 of 34 cancer types, with the highest sampling consensus in UCEC. Additionally, TSPY4 RNA expression shows 4,429 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UCEC, and TGCT as cancer lineages where TSPY4 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 TSPY4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TSPY4 survival associations across molecular data types. TSPY4 RNA expression shows survival associations in the most cancer types (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TSPY4 RNA expression–survival associations across cancer types. High TSPY4 expression shows unfavorable associations in UCEC, LIHC and HNSC, but favorable associations in SKCM. The UCEC 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 UCEC as the clearest survival context for TSPY4 RNA expression.
This table shows molecular features associated with TSPY4 in patient tissues and cancer cell lines. In patient samples, TSPY4 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, TSPY4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS.
