Q-omics provides the consensus-scored KRTAP2-1 profile across patient tissues and cancer cell-line models. KRTAP2-1 expression is associated with patient survival in 8 of 34 cancer types, with the highest sampling consensus in SKCM. Additionally, KRTAP2-1 RNA expression shows 4,353 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight SKCM, and STAD as cancer lineages where KRTAP2-1 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 KRTAP2-1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes KRTAP2-1 survival associations across molecular data types. KRTAP2-1 RNA expression shows survival associations in the most cancer types (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible KRTAP2-1 RNA expression–survival associations across cancer types. High KRTAP2-1 expression shows unfavorable associations in SKCM, THCA, KIRC, ESCA, LGG and PCPG. The SKCM 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 SKCM as the clearest survival context for KRTAP2-1 RNA expression.
This table shows molecular features associated with KRTAP2-1 in patient tissues and cancer cell lines. In patient samples, KRTAP2-1 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, KRTAP2-1 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 LIVER.
