Q-omics provides the consensus-scored ROR2 profile across patient tissues and cancer cell-line models. ROR2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, ROR2 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, ROR2 RNA expression shows 16,697 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight LGG, HNSC, and PDAC as cancer lineages where ROR2 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 ROR2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ROR2 survival associations across molecular data types. ROR2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (10) 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 ROR2 RNA expression–survival associations across cancer types. High ROR2 expression shows unfavorable associations in LGG, KIRP, THCA and KIRC, but favorable associations in SCLC and DLBC. The LGG 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 LGG as the clearest survival context for ROR2 RNA expression.
This table summarizes ROR2 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 HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for ROR2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ROR2 shows lower tumor expression in KICH, THCA and BLCA and higher tumor expression in HNSC, LUAD and BRCA. The HNSC box plot shows higher ROR2 RNA expression in tumor versus normal tissue (log2 FC = +1.778, t-test p < 0.001).
This table shows molecular features associated with ROR2 in patient tissues and cancer cell lines. In patient samples, ROR2 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, ROR2 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 LUNG_NSCLC_LUSC and BLOOD_Lymphoma.
