Q-omics provides the consensus-scored PAFAH1B2 profile across patient tissues and cancer cell-line models. PAFAH1B2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, PAFAH1B2 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, PAFAH1B2 RNA expression shows 19,536 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, HNSC, and ACC as cancer lineages where PAFAH1B2 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 PAFAH1B2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PAFAH1B2 survival associations across molecular data types. PAFAH1B2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) 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 PAFAH1B2 RNA expression–survival associations across cancer types. High PAFAH1B2 expression shows unfavorable associations in LIHC, PAAD, HNSC, UVM, LUAD and BLCA. 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 PAFAH1B2 RNA expression.
This table summarizes PAFAH1B2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PAFAH1B2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PAFAH1B2 shows higher tumor expression in HNSC, STAD, LIHC, BLCA, CHOL and LUAD. The HNSC box plot shows higher PAFAH1B2 RNA expression in tumor versus normal tissue (log2 FC = +1.052, t-test p < 0.001).
This table shows molecular features associated with PAFAH1B2 in patient tissues and cancer cell lines. In patient samples, PAFAH1B2 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, PAFAH1B2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and LARGE_INTESTINE.
