Q-omics provides the consensus-scored SIGLEC16 profile across patient tissues and cancer cell-line models. SIGLEC16 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SIGLEC16 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, SIGLEC16 RNA expression shows 12,384 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, and ACC as cancer lineages where SIGLEC16 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 SIGLEC16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SIGLEC16 survival associations across molecular data types. SIGLEC16 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SIGLEC16 RNA expression–survival associations across cancer types. High SIGLEC16 expression shows unfavorable associations in KIRC, LGG, ACC, HNSC and LAML, but favorable associations in SKCM. The KIRC 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 KIRC as the clearest survival context for SIGLEC16 RNA expression.
This table summarizes SIGLEC16 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for SIGLEC16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SIGLEC16 shows lower tumor expression in LUSC, LUAD, BLCA, BRCA and COAD and higher tumor expression in KIRC. The KIRC box plot shows higher SIGLEC16 RNA expression in tumor versus normal tissue (log2 FC = +0.498, t-test p < 0.001).
This table shows molecular features associated with SIGLEC16 in patient tissues and cancer cell lines. In patient samples, SIGLEC16 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, SIGLEC16 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in CNS and LUNG_NSCLC_LUAD.
