Q-omics provides the consensus-scored RP9 profile across patient tissues and cancer cell-line models. RP9 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, RP9 is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, RP9 RNA expression shows 19,051 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, HNSC, and ACC as cancer lineages where RP9 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 RP9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RP9 survival associations across molecular data types. RP9 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (6) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RP9 RNA expression–survival associations across cancer types. High RP9 expression shows unfavorable associations in KIRC, KICH, ACC, MESO, LIHC and LGG. The KIRC 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 KIRC as the clearest survival context for RP9 RNA expression.
This table summarizes RP9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for RP9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RP9 shows higher tumor expression in HNSC, KIRC, COAD, STAD, BLCA and LIHC. The HNSC box plot shows higher RP9 RNA expression in tumor versus normal tissue (log2 FC = +0.882, t-test p < 0.001).
This table shows molecular features associated with RP9 in patient tissues and cancer cell lines. In patient samples, RP9 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, RP9 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 BONE and BLOOD_Leukemia.
