Q-omics provides the consensus-scored HYKK profile across patient tissues and cancer cell-line models. HYKK expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, HYKK is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, HYKK RNA expression shows 19,615 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRP, KIRC, and LSCC as cancer lineages where HYKK 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 HYKK — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HYKK survival associations across molecular data types. HYKK RNA expression shows survival associations in the most cancer types (21), followed by mutation status (1) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible HYKK RNA expression–survival associations across cancer types. High HYKK expression shows unfavorable associations in KICH, LGG and STAD, but favorable associations in KIRP, UVM and OV. The KIRP Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRP as the clearest survival context for HYKK RNA expression.
This table summarizes HYKK tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 2. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for HYKK. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HYKK shows lower tumor expression in KIRC, KIRP, THCA and KICH and higher tumor expression in LIHC and LUSC. The KIRC box plot shows higher HYKK RNA expression in normal versus tumor tissue (log2 FC = −1.408, t-test p < 0.001).
This table shows molecular features associated with HYKK in patient tissues and cancer cell lines. In patient samples, HYKK shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, HYKK RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LUNG_NSCLC_LUAD.
