Q-omics provides the consensus-scored SYPL2 profile across patient tissues and cancer cell-line models. SYPL2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, SYPL2 is differentially expressed in 15, with the highest sampling consensus in KICH. Additionally, SYPL2 RNA expression shows 15,963 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight BLCA, KICH, and KIRP as cancer lineages where SYPL2 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 SYPL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SYPL2 survival associations across molecular data types. SYPL2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SYPL2 RNA expression–survival associations across cancer types. High SYPL2 expression shows unfavorable associations in BLCA, STAD and COAD, but favorable associations in KIRP, UVM and UCS. The BLCA 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 BLCA as the clearest survival context for SYPL2 RNA expression.
This table summarizes SYPL2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 3. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SYPL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SYPL2 shows lower tumor expression in KICH, THCA, HNSC, LUSC, KIRC and BLCA. The KICH box plot shows higher SYPL2 RNA expression in normal versus tumor tissue (log2 FC = −1.926, t-test p < 0.001).
This table shows molecular features associated with SYPL2 in patient tissues and cancer cell lines. In patient samples, SYPL2 shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, SYPL2 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 BONE.
