zinc fingers and homeoboxes 2Genealiases: AFR1 · RAF
Q-omics provides the consensus-scored ZHX2 profile across patient tissues and cancer cell-line models. ZHX2 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ZHX2 is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, ZHX2 RNA expression shows 20,211 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, KICH, and ACC as cancer lineages where ZHX2 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 ZHX2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZHX2 survival associations across molecular data types. ZHX2 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (8) 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 ZHX2 RNA expression–survival associations across cancer types. High ZHX2 expression shows unfavorable associations in ACC, but favorable associations in KIRC, HNSC, LGG, UCEC and UCS. The KIRC 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 KIRC as the clearest survival context for ZHX2 RNA expression.
This table summarizes ZHX2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ZHX2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZHX2 shows lower tumor expression in KICH, BLCA and LUAD and higher tumor expression in KIRC, LIHC and KIRP. The KICH box plot shows higher ZHX2 RNA expression in normal versus tumor tissue (log2 FC = −1.825, t-test p < 0.001).
This table shows molecular features associated with ZHX2 in patient tissues and cancer cell lines. In patient samples, ZHX2 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, ZHX2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Lymphoma.
