Q-omics provides the consensus-scored RILP profile across patient tissues and cancer cell-line models. RILP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, RILP is differentially expressed in 12, with the highest sampling consensus in COAD. Additionally, RILP protein abundance shows 21,701 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, COAD, and LSCC as cancer lineages where RILP 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 RILP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RILP survival associations across molecular data types. RILP RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) 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 RILP RNA expression–survival associations across cancer types. High RILP expression shows unfavorable associations in UVM and LGG, but favorable associations in MESO, BLCA, KIRP and LIHC. The UVM Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify UVM as the clearest survival context for RILP RNA expression.
This table summarizes RILP 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 7. The strongest signals are observed in COAD for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for RILP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RILP shows lower tumor expression in COAD, LUSC, THCA, LUAD and BRCA and higher tumor expression in KICH. The COAD box plot shows higher RILP RNA expression in normal versus tumor tissue (log2 FC = −1.437, t-test p < 0.001).
This table shows molecular features associated with RILP in patient tissues and cancer cell lines. In patient samples, RILP 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, RILP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BLOOD_Leukemia.
