Q-omics provides the consensus-scored RPS16 profile across patient tissues and cancer cell-line models. RPS16 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, RPS16 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, RPS16 protein abundance shows 29,491 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, KIRC, and GBM as cancer lineages where RPS16 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 RPS16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RPS16 survival associations across molecular data types. RPS16 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RPS16 RNA expression–survival associations across cancer types. High RPS16 expression shows unfavorable associations in ACC, LIHC, KIRP, LUAD and PAAD, but favorable associations in MESO. 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 RPS16 RNA expression.
This table summarizes RPS16 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 10. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for RPS16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RPS16 shows lower tumor expression in UCEC and higher tumor expression in KIRC, COAD, KIRP, LIHC and CHOL. The KIRC box plot shows higher RPS16 RNA expression in tumor versus normal tissue (log2 FC = +0.792, t-test p < 0.001).
This table shows molecular features associated with RPS16 in patient tissues and cancer cell lines. In patient samples, RPS16 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, RPS16 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and CNS.
