Q-omics provides the consensus-scored RCN3 profile across patient tissues and cancer cell-line models. RCN3 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, RCN3 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, RCN3 protein abundance shows 28,994 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, HNSC, and PDAC as cancer lineages where RCN3 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 RCN3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RCN3 survival associations across molecular data types. RCN3 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) 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 RCN3 RNA expression–survival associations across cancer types. High RCN3 expression shows unfavorable associations in ACC, KIRC, BLCA, KIRP and LGG, but favorable associations in LUAD. 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 RCN3 RNA expression.
This table summarizes RCN3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for RCN3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RCN3 shows lower tumor expression in KICH and higher tumor expression in HNSC, COAD, KIRC, LUAD and STAD. The HNSC box plot shows higher RCN3 RNA expression in tumor versus normal tissue (log2 FC = +2.697, t-test p < 0.001).
This table shows molecular features associated with RCN3 in patient tissues and cancer cell lines. In patient samples, RCN3 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, RCN3 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 BLOOD_Leukemia and SOFT_TISSUE.
