Q-omics provides the consensus-scored TNP2 profile across patient tissues and cancer cell-line models. TNP2 expression is associated with patient survival in 11 of 34 cancer types, with the highest sampling consensus in KICH. Additionally, TNP2 RNA expression shows 6,428 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight KICH, and STAD as cancer lineages where TNP2 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 TNP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TNP2 survival associations across molecular data types. TNP2 RNA expression shows survival associations in the most cancer types (11), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TNP2 RNA expression–survival associations across cancer types. High TNP2 expression shows unfavorable associations in KICH, ACC, UVM, THYM, LGG and KIRC. 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 TNP2 RNA expression.
This table shows molecular features associated with TNP2 in patient tissues and cancer cell lines. In patient samples, TNP2 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, TNP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in CNS and LUNG_NSCLC_LUAD.
