Q-omics provides the consensus-scored ZFAT profile across patient tissues and cancer cell-line models. ZFAT expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ZFAT is differentially expressed in 12, with the highest sampling consensus in LIHC. Additionally, ZFAT RNA expression shows 20,235 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, LIHC, and ACC as cancer lineages where ZFAT 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 ZFAT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZFAT survival associations across molecular data types. ZFAT RNA expression shows survival associations in the most cancer types (23), followed by mutation status (7) 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 ZFAT RNA expression–survival associations across cancer types. High ZFAT expression shows unfavorable associations in UVM, COAD, UCEC and LIHC, but favorable associations in KIRC and THYM. 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 ZFAT RNA expression.
This table summarizes ZFAT 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 LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ZFAT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZFAT shows lower tumor expression in KIRP and KICH and higher tumor expression in LIHC, HNSC, LUAD and CHOL. The LIHC box plot shows higher ZFAT RNA expression in tumor versus normal tissue (log2 FC = +1.040, t-test p < 0.001).
This table shows molecular features associated with ZFAT in patient tissues and cancer cell lines. In patient samples, ZFAT 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, ZFAT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BONE and UPPER_AERODIGESTIVE_TRACT.
