Q-omics provides the consensus-scored RPP30 profile across patient tissues and cancer cell-line models. RPP30 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, RPP30 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, RPP30 protein abundance shows 28,208 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where RPP30 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 RPP30 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RPP30 survival associations across molecular data types. RPP30 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (5) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RPP30 RNA expression–survival associations across cancer types. High RPP30 expression shows unfavorable associations in ACC, KICH, LIHC, LUAD and KIRP, but favorable associations in LGG. 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 RPP30 RNA expression.
This table summarizes RPP30 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 6. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RPP30. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RPP30 shows lower tumor expression in KICH and higher tumor expression in HNSC, LIHC, LUAD, LUSC and CHOL. The HNSC box plot shows higher RPP30 RNA expression in tumor versus normal tissue (log2 FC = +0.443, t-test p < 0.001).
This table shows molecular features associated with RPP30 in patient tissues and cancer cell lines. In patient samples, RPP30 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, RPP30 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 SOFT_TISSUE and BLOOD_Leukemia.
