Q-omics provides the consensus-scored ZBTB9 profile across patient tissues and cancer cell-line models. ZBTB9 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, ZBTB9 is differentially expressed in 9, with the highest sampling consensus in STAD. Additionally, ZBTB9 RNA expression shows 15,356 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight LIHC, STAD, and LSCC as cancer lineages where ZBTB9 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 ZBTB9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZBTB9 survival associations across molecular data types. ZBTB9 RNA expression shows survival associations in the most cancer types (20), 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 ZBTB9 RNA expression–survival associations across cancer types. High ZBTB9 expression shows unfavorable associations in LIHC, DLBC and SKCM, but favorable associations in PAAD, ESCA and THYM. The LIHC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify LIHC as the clearest survival context for ZBTB9 RNA expression.
This table summarizes ZBTB9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 4. The strongest signals are observed in STAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ZBTB9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZBTB9 shows lower tumor expression in KICH and higher tumor expression in STAD, LIHC, LUSC, LUAD and CHOL. The STAD box plot shows higher ZBTB9 RNA expression in tumor versus normal tissue (log2 FC = +0.076, t-test p < 0.001).
This table shows molecular features associated with ZBTB9 in patient tissues and cancer cell lines. In patient samples, ZBTB9 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ZBTB9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
