Q-omics provides the consensus-scored RPF1 profile across patient tissues and cancer cell-line models. RPF1 expression is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, RPF1 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, RPF1 protein abundance shows 27,545 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRP, HNSC, and LSCC as cancer lineages where RPF1 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 RPF1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RPF1 survival associations across molecular data types. RPF1 RNA expression shows survival associations in the most cancer types (30), followed by mutation status (2) and mass-spec protein abundance (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RPF1 RNA expression–survival associations across cancer types. High RPF1 expression shows unfavorable associations in KIRP, ACC, KICH, LIHC and LGG, but favorable associations in BRCA. The KIRP Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify KIRP as the clearest survival context for RPF1 RNA expression.
This table summarizes RPF1 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 10. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RPF1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RPF1 shows lower tumor expression in KICH and THCA and higher tumor expression in HNSC, LIHC, STAD and BLCA. The HNSC box plot shows higher RPF1 RNA expression in tumor versus normal tissue (log2 FC = +0.413, t-test p < 0.001).
This table shows molecular features associated with RPF1 in patient tissues and cancer cell lines. In patient samples, RPF1 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, RPF1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
