Q-omics provides the consensus-scored RPS13 profile across patient tissues and cancer cell-line models. RPS13 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, RPS13 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, RPS13 protein abundance shows 38,833 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, KIRC, and GBM as cancer lineages where RPS13 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 RPS13 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RPS13 survival associations across molecular data types. RPS13 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RPS13 RNA expression–survival associations across cancer types. High RPS13 expression shows unfavorable associations in ACC, KICH, LIHC, KIRP and SCLC, 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 RPS13 RNA expression.
This table summarizes RPS13 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 13. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RPS13. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RPS13 shows lower tumor expression in BLCA and UCEC and higher tumor expression in KIRC, COAD, LIHC and CHOL. The KIRC box plot shows higher RPS13 RNA expression in tumor versus normal tissue (log2 FC = +0.736, t-test p < 0.001).
This table shows molecular features associated with RPS13 in patient tissues and cancer cell lines. In patient samples, RPS13 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, RPS13 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 KIDNEY and BLOOD_Leukemia.
