Q-omics provides the consensus-scored SYF2 profile across patient tissues and cancer cell-line models. SYF2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SYF2 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, SYF2 protein abundance shows 20,025 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where SYF2 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 SYF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SYF2 survival associations across molecular data types. SYF2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SYF2 RNA expression–survival associations across cancer types. High SYF2 expression shows unfavorable associations in ACC, KICH, LIHC and LGG, but favorable associations in KIRC and MESO. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for SYF2 RNA expression.
This table summarizes SYF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SYF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SYF2 shows lower tumor expression in KICH, UCEC and COAD and higher tumor expression in HNSC, LIHC and CHOL. The HNSC box plot shows higher SYF2 RNA expression in tumor versus normal tissue (log2 FC = +0.382, t-test p < 0.001).
This table shows molecular features associated with SYF2 in patient tissues and cancer cell lines. In patient samples, SYF2 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, SYF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUSC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
