Q-omics provides the consensus-scored ELOVL1 profile across patient tissues and cancer cell-line models. ELOVL1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ELOVL1 is differentially expressed in 11, with the highest sampling consensus in LIHC. Additionally, ELOVL1 protein abundance shows 21,652 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, LIHC, and GBM as cancer lineages where ELOVL1 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 ELOVL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ELOVL1 survival associations across molecular data types. ELOVL1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (1) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ELOVL1 RNA expression–survival associations across cancer types. High ELOVL1 expression shows unfavorable associations in ACC, LIHC, MESO, KICH, CESC and UCS. 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 ELOVL1 RNA expression.
This table summarizes ELOVL1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ELOVL1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ELOVL1 shows lower tumor expression in KICH and higher tumor expression in LIHC, KIRP, UCEC, BRCA and KIRC. The LIHC box plot shows higher ELOVL1 RNA expression in tumor versus normal tissue (log2 FC = +1.832, t-test p < 0.001).
This table shows molecular features associated with ELOVL1 in patient tissues and cancer cell lines. In patient samples, ELOVL1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, ELOVL1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
