Q-omics provides the consensus-scored RFLNB profile across patient tissues and cancer cell-line models. RFLNB expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, RFLNB is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, RFLNB protein abundance shows 21,504 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRP, HNSC, and LSCC as cancer lineages where RFLNB 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 RFLNB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RFLNB survival associations across molecular data types. RFLNB RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) 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 RFLNB RNA expression–survival associations across cancer types. High RFLNB expression shows unfavorable associations in KIRP, MESO, BLCA, UVM and SARC, but favorable associations in KIRC. The KIRP 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 KIRP as the clearest survival context for RFLNB RNA expression.
This table summarizes RFLNB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, 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 RFLNB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RFLNB shows lower tumor expression in LUAD, BLCA, LUSC and BRCA and higher tumor expression in HNSC and KIRC. The HNSC box plot shows higher RFLNB RNA expression in tumor versus normal tissue (log2 FC = +1.943, t-test p < 0.001).
This table shows molecular features associated with RFLNB in patient tissues and cancer cell lines. In patient samples, RFLNB 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, RFLNB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SKIN.
