Q-omics provides the consensus-scored TBC1D3H profile across patient tissues and cancer cell-line models. TBC1D3H expression is associated with patient survival in 10 of 34 cancer types, with the highest sampling consensus in READ. Additionally, TBC1D3H RNA expression shows 4,110 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight READ, and STAD as cancer lineages where TBC1D3H 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 TBC1D3H — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TBC1D3H survival associations across molecular data types. TBC1D3H 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 TBC1D3H RNA expression–survival associations across cancer types. High TBC1D3H expression shows unfavorable associations in READ, KIRP, KIRC, MESO, LAML and GBM. The READ 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 READ as the clearest survival context for TBC1D3H RNA expression.
This table shows molecular features associated with TBC1D3H in patient tissues and cancer cell lines. In patient samples, TBC1D3H 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, TBC1D3H RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and OVARY.
