Q-omics provides the consensus-scored SYT13 profile across patient tissues and cancer cell-line models. SYT13 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, SYT13 is differentially expressed in 12, with the highest sampling consensus in BRCA. Additionally, SYT13 protein abundance shows 21,416 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight CESC, BRCA, and GBM as cancer lineages where SYT13 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 SYT13 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SYT13 survival associations across molecular data types. SYT13 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (3) 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 SYT13 RNA expression–survival associations across cancer types. High SYT13 expression shows unfavorable associations in CESC, UCEC, LUAD and UVM, but favorable associations in LGG and KIRP. The CESC 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 CESC as the clearest survival context for SYT13 RNA expression.
This table summarizes SYT13 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 7. The strongest signals are observed in BRCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SYT13. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SYT13 shows lower tumor expression in COAD and higher tumor expression in BRCA, LUAD, HNSC, KICH and STAD. The BRCA box plot shows higher SYT13 RNA expression in tumor versus normal tissue (log2 FC = +2.846, t-test p < 0.001).
This table shows molecular features associated with SYT13 in patient tissues and cancer cell lines. In patient samples, SYT13 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, SYT13 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 BLOOD_Leukemia and LUNG_SCLC.
