Q-omics provides the consensus-scored RFPL1 profile across patient tissues and cancer cell-line models. RFPL1 expression is associated with patient survival in 18 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, RFPL1 is differentially expressed in 6, with the highest sampling consensus in LIHC. Additionally, RFPL1 RNA expression shows 9,714 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight HNSC, LIHC, and TGCT as cancer lineages where RFPL1 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 RFPL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RFPL1 survival associations across molecular data types. RFPL1 RNA expression shows survival associations in the most cancer types (18), followed by mutation status (7) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RFPL1 RNA expression–survival associations across cancer types. High RFPL1 expression shows unfavorable associations in KIRC, THYM, UCEC and ACC, but favorable associations in HNSC and LUSC. The HNSC 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 HNSC as the clearest survival context for RFPL1 RNA expression.
This table summarizes RFPL1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 6. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for RFPL1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RFPL1 shows lower tumor expression in LIHC, CHOL and PRAD and higher tumor expression in BRCA, KIRC and LUSC. The LIHC box plot shows higher RFPL1 RNA expression in normal versus tumor tissue (log2 FC = −0.203, t-test p < 0.001).
This table shows molecular features associated with RFPL1 in patient tissues and cancer cell lines. In patient samples, RFPL1 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, RFPL1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BONE.
