Q-omics provides the consensus-scored YIF1A profile across patient tissues and cancer cell-line models. YIF1A 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, YIF1A is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, YIF1A protein abundance shows 23,566 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, HNSC, and PDAC as cancer lineages where YIF1A 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 YIF1A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes YIF1A survival associations across molecular data types. YIF1A RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible YIF1A RNA expression–survival associations across cancer types. High YIF1A expression shows unfavorable associations in ACC, BLCA, HNSC, UVM, LIHC and KICH. 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 YIF1A RNA expression.
This table summarizes YIF1A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for YIF1A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. YIF1A shows higher tumor expression in HNSC, KIRC, BLCA, COAD, KIRP and LUAD. The HNSC box plot shows higher YIF1A RNA expression in tumor versus normal tissue (log2 FC = +1.063, t-test p < 0.001).
This table shows molecular features associated with YIF1A in patient tissues and cancer cell lines. In patient samples, YIF1A shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, YIF1A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Lymphoma.
