Q-omics provides the consensus-scored TSPY2 profile across patient tissues and cancer cell-line models. TSPY2 expression is associated with patient survival in 11 of 34 cancer types, with the highest sampling consensus in KICH. Additionally, TSPY2 RNA expression shows 6,673 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KICH, and TGCT as cancer lineages where TSPY2 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 TSPY2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TSPY2 survival associations across molecular data types. TSPY2 RNA expression shows survival associations in the most cancer types (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TSPY2 RNA expression–survival associations across cancer types. High TSPY2 expression shows unfavorable associations in KICH, BLCA, UCEC, LIHC, LUAD and ESCA. 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 TSPY2 RNA expression.
This table shows molecular features associated with TSPY2 in patient tissues and cancer cell lines. In patient samples, TSPY2 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, TSPY2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
