elaC ribonuclease Z 2Genealiases: COXPD17 · ELC2 · HPC2
Q-omics provides the consensus-scored ELAC2 profile across patient tissues and cancer cell-line models. ELAC2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ELAC2 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, ELAC2 protein abundance shows 23,059 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, HNSC, and LSCC as cancer lineages where ELAC2 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 ELAC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ELAC2 survival associations across molecular data types. ELAC2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (9) 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 ELAC2 RNA expression–survival associations across cancer types. High ELAC2 expression shows unfavorable associations in UVM, KICH, LGG, MESO, ACC and LUAD. The UVM Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .004). Together, the overview and detailed table identify UVM as the clearest survival context for ELAC2 RNA expression.
This table summarizes ELAC2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ELAC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ELAC2 shows lower tumor expression in KICH and higher tumor expression in HNSC, COAD, KIRP, STAD and CHOL. The HNSC box plot shows higher ELAC2 RNA expression in tumor versus normal tissue (log2 FC = +0.566, t-test p < 0.001).
This table shows molecular features associated with ELAC2 in patient tissues and cancer cell lines. In patient samples, ELAC2 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ELAC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BLOOD_Leukemia.
