Q-omics provides the consensus-scored CLEC10A profile across patient tissues and cancer cell-line models. CLEC10A expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, CLEC10A is differentially expressed in 12, with the highest sampling consensus in COAD. Additionally, CLEC10A RNA expression shows 22,135 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, COAD, and LSCC as cancer lineages where CLEC10A 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 CLEC10A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CLEC10A survival associations across molecular data types. CLEC10A RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CLEC10A RNA expression–survival associations across cancer types. High CLEC10A expression shows favorable associations in HNSC, LUAD, CESC, SKCM, UCEC and SARC. 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 CLEC10A RNA expression.
This table summarizes CLEC10A 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 4. The strongest signals are observed in BLCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for CLEC10A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CLEC10A shows lower tumor expression in COAD, BLCA, KICH, LUAD, LUSC and HNSC. The COAD box plot shows higher CLEC10A RNA expression in normal versus tumor tissue (log2 FC = −2.740, t-test p < 0.001).
This table shows molecular features associated with CLEC10A in patient tissues and cancer cell lines. In patient samples, CLEC10A 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, CLEC10A 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 SOFT_TISSUE and CNS.
