Q-omics provides the consensus-scored ELOB profile across patient tissues and cancer cell-line models. ELOB expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ELOB is differentially expressed in 14, with the highest sampling consensus in KIRP. Additionally, ELOB protein abundance shows 35,721 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight UVM, KIRP, and LUAD as cancer lineages where ELOB 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 ELOB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ELOB survival associations across molecular data types. ELOB RNA expression shows survival associations in the most cancer types (25), followed by mutation status (1) and mass-spec protein abundance (12). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ELOB RNA expression–survival associations across cancer types. High ELOB expression shows unfavorable associations in UVM, UCS, KIRC, HNSC and ACC, but favorable associations in CESC. The UVM 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 UVM as the clearest survival context for ELOB RNA expression.
This table summarizes ELOB 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 13. The strongest signals are observed in KIRP for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ELOB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ELOB shows higher tumor expression in KIRP, COAD, LIHC, KIRC, HNSC and STAD. The KIRP box plot shows higher ELOB RNA expression in tumor versus normal tissue (log2 FC = +0.842, t-test p < 0.001).
This table shows molecular features associated with ELOB in patient tissues and cancer cell lines. In patient samples, ELOB shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, ELOB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in SKIN and SOFT_TISSUE.
