Q-omics provides the consensus-scored LYRM4 profile across patient tissues and cancer cell-line models. LYRM4 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, LYRM4 is differentially expressed in 16, with the highest sampling consensus in COAD. Additionally, LYRM4 RNA expression shows 18,675 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, COAD, and ACC as cancer lineages where LYRM4 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 LYRM4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes LYRM4 survival associations across molecular data types. LYRM4 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (1) 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 LYRM4 RNA expression–survival associations across cancer types. High LYRM4 expression shows unfavorable associations in ACC, LIHC, KIRP, KICH and SARC, but favorable associations in UVM. The UVM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify UVM as the clearest survival context for LYRM4 RNA expression.
This table summarizes LYRM4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 5. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for LYRM4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. LYRM4 shows lower tumor expression in KICH and higher tumor expression in COAD, LIHC, HNSC, KIRP and KIRC. The COAD box plot shows higher LYRM4 RNA expression in tumor versus normal tissue (log2 FC = +0.994, t-test p < 0.001).
This table shows molecular features associated with LYRM4 in patient tissues and cancer cell lines. In patient samples, LYRM4 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, LYRM4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BONE.
