sialic acid binding Ig like lectin 1Genealiases: CD169 · SIGLEC-1 · SN
Q-omics provides the consensus-scored SIGLEC1 profile across patient tissues and cancer cell-line models. SIGLEC1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, SIGLEC1 is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, SIGLEC1 protein abundance shows 24,126 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where SIGLEC1 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 SIGLEC1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SIGLEC1 survival associations across molecular data types. SIGLEC1 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (7) 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 SIGLEC1 RNA expression–survival associations across cancer types. High SIGLEC1 expression shows unfavorable associations in KIRC, LGG and ACC, but favorable associations in SKCM, CESC and HNSC. The SKCM 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 SKCM as the clearest survival context for SIGLEC1 RNA expression.
This table summarizes SIGLEC1 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 11. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SIGLEC1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SIGLEC1 shows lower tumor expression in LUAD, COAD and LUSC and higher tumor expression in KIRC, KIRP and HNSC. The KIRC box plot shows higher SIGLEC1 RNA expression in tumor versus normal tissue (log2 FC = +1.955, t-test p < 0.001).
This table shows molecular features associated with SIGLEC1 in patient tissues and cancer cell lines. In patient samples, SIGLEC1 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, SIGLEC1 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 LARGE_INTESTINE and OVARY.
