Q-omics provides the consensus-scored INKA2 profile across patient tissues and cancer cell-line models. INKA2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, INKA2 is differentially expressed in 15, with the highest sampling consensus in BLCA. Additionally, INKA2 RNA expression shows 18,830 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight HNSC, BLCA, and UVM as cancer lineages where INKA2 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 INKA2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes INKA2 survival associations across molecular data types. INKA2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible INKA2 RNA expression–survival associations across cancer types. High INKA2 expression shows unfavorable associations in LIHC, but favorable associations in HNSC, KIRC, PAAD, CESC and READ. The HNSC 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 HNSC as the clearest survival context for INKA2 RNA expression.
This table summarizes INKA2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 4. The strongest signals are observed in BLCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for INKA2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. INKA2 shows lower tumor expression in BLCA, KICH, LUAD, KIRP and THCA and higher tumor expression in LIHC. The BLCA box plot shows higher INKA2 RNA expression in normal versus tumor tissue (log2 FC = −0.963, t-test p < 0.001).
This table shows molecular features associated with INKA2 in patient tissues and cancer cell lines. In patient samples, INKA2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, INKA2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in STOMACH, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
