Q-omics provides the consensus-scored COLCA2 profile across patient tissues and cancer cell-line models. COLCA2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, COLCA2 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, COLCA2 RNA expression shows 17,496 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight ACC, HNSC, and TGCT as cancer lineages where COLCA2 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 COLCA2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes COLCA2 survival associations across molecular data types. COLCA2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible COLCA2 RNA expression–survival associations across cancer types. High COLCA2 expression shows unfavorable associations in LIHC and UVM, but favorable associations in ACC, KIRP, LUAD and UCEC. The ACC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify ACC as the clearest survival context for COLCA2 RNA expression.
This table summarizes COLCA2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for COLCA2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. COLCA2 shows lower tumor expression in HNSC, THCA, BLCA, UCEC and BRCA and higher tumor expression in LIHC. The HNSC box plot shows higher COLCA2 RNA expression in normal versus tumor tissue (log2 FC = −1.010, t-test p < 0.001).
This table shows molecular features associated with COLCA2 in patient tissues and cancer cell lines. In patient samples, COLCA2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, COLCA2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BONE and STOMACH.
