Q-omics provides the consensus-scored SIGLEC10 profile across patient tissues and cancer cell-line models. SIGLEC10 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, SIGLEC10 is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, SIGLEC10 RNA expression shows 20,081 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where SIGLEC10 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 SIGLEC10 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SIGLEC10 survival associations across molecular data types. SIGLEC10 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SIGLEC10 RNA expression–survival associations across cancer types. High SIGLEC10 expression shows unfavorable associations in UVM, LGG and ACC, but favorable associations in SKCM, HNSC and CESC. 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 SIGLEC10 RNA expression.
This table summarizes SIGLEC10 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 2. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for SIGLEC10. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SIGLEC10 shows higher tumor expression in KIRC, KIRP, STAD, HNSC, THCA and BRCA. The KIRC box plot shows higher SIGLEC10 RNA expression in tumor versus normal tissue (log2 FC = +2.680, t-test p < 0.001).
This table shows molecular features associated with SIGLEC10 in patient tissues and cancer cell lines. In patient samples, SIGLEC10 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, SIGLEC10 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in OESOPHAGUS and BLOOD_Lymphoma.
