Q-omics provides the consensus-scored SYCN profile across patient tissues and cancer cell-line models. SYCN expression is associated with patient survival in 18 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, SYCN is differentially expressed in 5, with the highest sampling consensus in ESCA. Additionally, SYCN RNA expression shows 8,133 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight SCLC, ESCA, and PDAC as cancer lineages where SYCN 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 SYCN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SYCN survival associations across molecular data types. SYCN RNA expression shows survival associations in the most cancer types (18), followed by mutation status (3) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SYCN RNA expression–survival associations across cancer types. High SYCN expression shows unfavorable associations in SCLC, READ, COAD, LUAD and THYM, but favorable associations in UCS. The SCLC 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 SCLC as the clearest survival context for SYCN RNA expression.
This table summarizes SYCN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5, while mass-spec protein shows differences in 1. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SYCN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SYCN shows lower tumor expression in ESCA, HNSC, LUAD and STAD and higher tumor expression in LIHC. The ESCA box plot shows higher SYCN RNA expression in normal versus tumor tissue (log2 FC = −1.063, t-test p < 0.001).
This table shows molecular features associated with SYCN in patient tissues and cancer cell lines. In patient samples, SYCN 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, SYCN RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Lymphoma.
