Q-omics provides the consensus-scored YKT6 profile across patient tissues and cancer cell-line models. YKT6 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, YKT6 is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, YKT6 RNA expression shows 18,707 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight HNSC, and ACC as cancer lineages where YKT6 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 YKT6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes YKT6 survival associations across molecular data types. YKT6 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (2) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible YKT6 RNA expression–survival associations across cancer types. High YKT6 expression shows unfavorable associations in HNSC, MESO, KICH, BLCA, ACC and CESC. The HNSC 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 HNSC as the clearest survival context for YKT6 RNA expression.
This table summarizes YKT6 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for YKT6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. YKT6 shows higher tumor expression in HNSC, BLCA, STAD, LIHC, LUAD and KIRP. The HNSC box plot shows higher YKT6 RNA expression in tumor versus normal tissue (log2 FC = +1.238, t-test p < 0.001).
This table shows molecular features associated with YKT6 in patient tissues and cancer cell lines. In patient samples, YKT6 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, YKT6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and CNS.
