Q-omics provides the consensus-scored CCL27 profile across patient tissues and cancer cell-line models. CCL27 expression is associated with patient survival in 13 of 34 cancer types, with the highest sampling consensus in READ. Additionally, CCL27 RNA expression shows 4,141 significant gene co-expression associations, with the highest sampling consensus in SCLC. Together, these results highlight READ, and SCLC as cancer lineages where CCL27 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 CCL27 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CCL27 survival associations across molecular data types. CCL27 RNA expression shows survival associations in the most cancer types (13), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CCL27 RNA expression–survival associations across cancer types. High CCL27 expression shows unfavorable associations in READ, THCA, HNSC, ACC, BLCA and TGCT. The READ Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .009). Together, the overview and detailed table identify READ as the clearest survival context for CCL27 RNA expression.
This table shows molecular features associated with CCL27 in patient tissues and cancer cell lines. In patient samples, CCL27 shows the broadest associations at the RNA and protein expression levels, with SCLC recurring as the lineage with the largest associated feature set. In cancer cell lines, CCL27 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and CNS.
