Q-omics provides the consensus-scored TRIM49D1 profile across patient tissues and cancer cell-line models. TRIM49D1 expression is associated with patient survival in 6 of 34 cancer types, with the highest sampling consensus in BLCA. Additionally, TRIM49D1 RNA expression shows 7,140 significant gene co-expression associations, with the highest sampling consensus in LUSC. Together, these results highlight BLCA, and LUSC as cancer lineages where TRIM49D1 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 TRIM49D1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TRIM49D1 survival associations across molecular data types. TRIM49D1 RNA expression shows survival associations in the most cancer types (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TRIM49D1 RNA expression–survival associations across cancer types. High TRIM49D1 expression shows unfavorable associations in BLCA, KIRC, PCPG, THYM, STAD and SKCM. The BLCA Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify BLCA as the clearest survival context for TRIM49D1 RNA expression.
This table shows molecular features associated with TRIM49D1 in patient tissues and cancer cell lines. In patient samples, TRIM49D1 shows the broadest associations at the RNA and protein expression levels, with LUSC recurring as the lineage with the largest associated feature set. In cancer cell lines, TRIM49D1 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_Leukemia and PANCREAS.
