Q-omics provides the consensus-scored ARRB2 profile across patient tissues and cancer cell-line models. ARRB2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, ARRB2 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, ARRB2 protein abundance shows 20,106 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where ARRB2 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 ARRB2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ARRB2 survival associations across molecular data types. ARRB2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ARRB2 RNA expression–survival associations across cancer types. High ARRB2 expression shows unfavorable associations in UVM, KIRC, LUSC and LIHC, but favorable associations in SKCM and CESC. The SKCM 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 SKCM as the clearest survival context for ARRB2 RNA expression.
This table summarizes ARRB2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ARRB2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ARRB2 shows lower tumor expression in LUAD and LUSC and higher tumor expression in KIRC, HNSC, COAD and KIRP. The KIRC box plot shows higher ARRB2 RNA expression in tumor versus normal tissue (log2 FC = +1.551, t-test p < 0.001).
This table shows molecular features associated with ARRB2 in patient tissues and cancer cell lines. In patient samples, ARRB2 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, ARRB2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
