Q-omics provides the consensus-scored RCN2 profile across patient tissues and cancer cell-line models. RCN2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, RCN2 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, RCN2 protein abundance shows 26,907 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, HNSC, and LSCC as cancer lineages where RCN2 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 RCN2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RCN2 survival associations across molecular data types. RCN2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) 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 RCN2 RNA expression–survival associations across cancer types. High RCN2 expression shows unfavorable associations in UVM, ACC, LIHC, KICH and KIRP, but favorable associations in UCEC. The UVM 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 UVM as the clearest survival context for RCN2 RNA expression.
This table summarizes RCN2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 12. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for RCN2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RCN2 shows higher tumor expression in HNSC, BLCA, LIHC, COAD, THCA and LUSC. The HNSC box plot shows higher RCN2 RNA expression in tumor versus normal tissue (log2 FC = +1.079, t-test p < 0.001).
This table shows molecular features associated with RCN2 in patient tissues and cancer cell lines. In patient samples, RCN2 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, RCN2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Leukemia.
