Q-omics provides the consensus-scored YIF1B profile across patient tissues and cancer cell-line models. YIF1B expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, YIF1B is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, YIF1B RNA expression shows 19,488 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where YIF1B 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 YIF1B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes YIF1B survival associations across molecular data types. YIF1B RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) 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 YIF1B RNA expression–survival associations across cancer types. High YIF1B expression shows unfavorable associations in ACC, MESO, LIHC, BLCA and LGG, but favorable associations in SCLC. The ACC 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 ACC as the clearest survival context for YIF1B RNA expression.
This table summarizes YIF1B tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for YIF1B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. YIF1B shows higher tumor expression in HNSC, BLCA, COAD, STAD, LIHC and LUSC. The HNSC box plot shows higher YIF1B RNA expression in tumor versus normal tissue (log2 FC = +0.855, t-test p < 0.001).
This table shows molecular features associated with YIF1B in patient tissues and cancer cell lines. In patient samples, YIF1B 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, YIF1B 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 LUNG_NSCLC_LUSC and LARGE_INTESTINE.
