Q-omics provides the consensus-scored CTAG1B profile across patient tissues and cancer cell-line models. CTAG1B expression is associated with patient survival in 10 of 34 cancer types, with the highest sampling consensus in KICH. Additionally, CTAG1B RNA expression shows 2,930 significant gene co-expression associations, with the highest sampling consensus in BLCA. Together, these results highlight KICH, and BLCA as cancer lineages where CTAG1B 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 CTAG1B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CTAG1B survival associations across molecular data types. CTAG1B 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 CTAG1B RNA expression–survival associations across cancer types. High CTAG1B expression shows unfavorable associations in KICH, BLCA, MESO, TGCT, LUAD and COAD. 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 CTAG1B RNA expression.
This table shows molecular features associated with CTAG1B in patient tissues and cancer cell lines. In patient samples, CTAG1B shows the broadest associations at the RNA and protein expression levels, with BLCA recurring as the lineage with the largest associated feature set. In cancer cell lines, CTAG1B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC.
