Q-omics provides the consensus-scored RELB profile across patient tissues and cancer cell-line models. RELB expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, RELB is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, RELB protein abundance shows 26,064 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, KIRC, and GBM as cancer lineages where RELB 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 RELB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RELB survival associations across molecular data types. RELB RNA expression shows survival associations in the most cancer types (29), followed by mutation status (2) 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 RELB RNA expression–survival associations across cancer types. High RELB expression shows unfavorable associations in ACC, UVM, KIRP, LIHC and LGG, but favorable associations in SKCM. The ACC 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 ACC as the clearest survival context for RELB RNA expression.
This table summarizes RELB 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 8. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RELB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RELB shows higher tumor expression in KIRC, HNSC, LIHC, KIRP, STAD and THCA. The KIRC box plot shows higher RELB RNA expression in tumor versus normal tissue (log2 FC = +1.502, t-test p < 0.001).
This table shows molecular features associated with RELB in patient tissues and cancer cell lines. In patient samples, RELB shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, RELB 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 BREAST and LARGE_INTESTINE.
