Q-omics provides the consensus-scored KPNA6 profile across patient tissues and cancer cell-line models. KPNA6 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, KPNA6 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, KPNA6 RNA expression shows 20,348 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight SCLC, HNSC, and ACC as cancer lineages where KPNA6 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 KPNA6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes KPNA6 survival associations across molecular data types. KPNA6 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible KPNA6 RNA expression–survival associations across cancer types. High KPNA6 expression shows unfavorable associations in LGG, LIHC, ACC and BLCA, but favorable associations in SCLC and KIRC. The SCLC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify SCLC as the clearest survival context for KPNA6 RNA expression.
This table summarizes KPNA6 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 6. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for KPNA6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. KPNA6 shows lower tumor expression in KICH and higher tumor expression in HNSC, LIHC, LUAD, LUSC and BLCA. The HNSC box plot shows higher KPNA6 RNA expression in tumor versus normal tissue (log2 FC = +0.502, t-test p < 0.001).
This table shows molecular features associated with KPNA6 in patient tissues and cancer cell lines. In patient samples, KPNA6 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, KPNA6 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 OVARY and BLOOD_Leukemia.
