Q-omics provides the consensus-scored SYTL4 profile across patient tissues and cancer cell-line models. SYTL4 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, SYTL4 is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, SYTL4 RNA expression shows 19,643 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UVM, and KICH as cancer lineages where SYTL4 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 SYTL4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SYTL4 survival associations across molecular data types. SYTL4 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (5) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SYTL4 RNA expression–survival associations across cancer types. High SYTL4 expression shows unfavorable associations in UVM, ACC, LAML, MESO and KIRP, but favorable associations in BRCA. 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 SYTL4 RNA expression.
This table summarizes SYTL4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SYTL4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SYTL4 shows lower tumor expression in KICH, THCA, KIRC, UCEC, KIRP and COAD. The KICH box plot shows higher SYTL4 RNA expression in normal versus tumor tissue (log2 FC = −3.176, t-test p < 0.001).
This table shows molecular features associated with SYTL4 in patient tissues and cancer cell lines. In patient samples, SYTL4 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, SYTL4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
