sialic acid binding Ig like lectin 17, pseudogeneGenealiases: []
Q-omics provides the consensus-scored SIGLEC17P profile across patient tissues and cancer cell-line models. SIGLEC17P expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, SIGLEC17P is differentially expressed in 12, with the highest sampling consensus in LUAD. Additionally, SIGLEC17P RNA expression shows 19,571 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, LUAD, and LSCC as cancer lineages where SIGLEC17P 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 SIGLEC17P — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SIGLEC17P survival associations across molecular data types. SIGLEC17P RNA expression shows survival associations in the most cancer types (24). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SIGLEC17P RNA expression–survival associations across cancer types. High SIGLEC17P expression shows unfavorable associations in KIRP, but favorable associations in HNSC, BRCA, LUAD, CHOL and KIRC. The HNSC 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 HNSC as the clearest survival context for SIGLEC17P RNA expression.
This table summarizes SIGLEC17P tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for SIGLEC17P. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SIGLEC17P shows lower tumor expression in LUAD, BLCA, COAD, LUSC and UCEC and higher tumor expression in KIRC. The LUAD box plot shows higher SIGLEC17P RNA expression in normal versus tumor tissue (log2 FC = −1.578, t-test p < 0.001).
This table shows molecular features associated with SIGLEC17P in patient tissues and cancer cell lines. In patient samples, SIGLEC17P shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set.
