Q-omics provides the consensus-scored IDNK profile across patient tissues and cancer cell-line models. IDNK expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, IDNK is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, IDNK protein abundance shows 19,539 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight UCEC, KICH, and LUAD as cancer lineages where IDNK 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 IDNK — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IDNK survival associations across molecular data types. IDNK RNA expression shows survival associations in the most cancer types (29), followed by mutation status (3) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IDNK RNA expression–survival associations across cancer types. High IDNK expression shows unfavorable associations in UVM, but favorable associations in UCEC, BRCA, KIRC, SARC and SKCM. The UCEC 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 UCEC as the clearest survival context for IDNK RNA expression.
This table summarizes IDNK tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for IDNK. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IDNK shows lower tumor expression in KICH, KIRP, THCA, KIRC, BLCA and LUSC. The KICH box plot shows higher IDNK RNA expression in normal versus tumor tissue (log2 FC = −2.941, t-test p < 0.001).
This table shows molecular features associated with IDNK in patient tissues and cancer cell lines. In patient samples, IDNK shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, IDNK RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
