Q-omics provides the consensus-scored YWHAB profile across patient tissues and cancer cell-line models. YWHAB expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, YWHAB is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, YWHAB protein abundance shows 22,737 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight HNSC as cancer lineages where YWHAB 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 YWHAB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes YWHAB survival associations across molecular data types. YWHAB RNA expression shows survival associations in the most cancer types (28), followed by mutation status (2) 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 YWHAB RNA expression–survival associations across cancer types. High YWHAB expression shows unfavorable associations in HNSC, LIHC, UVM, ACC and MESO, but favorable associations in KIRC. The HNSC 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 HNSC as the clearest survival context for YWHAB RNA expression.
This table summarizes YWHAB 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 HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for YWHAB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. YWHAB shows higher tumor expression in HNSC, LIHC, STAD, BLCA, BRCA and CHOL. The HNSC box plot shows higher YWHAB RNA expression in tumor versus normal tissue (log2 FC = +0.629, t-test p < 0.001).
This table shows molecular features associated with YWHAB in patient tissues and cancer cell lines. In patient samples, YWHAB shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, YWHAB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in SKIN and UPPER_AERODIGESTIVE_TRACT.
