Q-omics provides the consensus-scored COLGALT1 profile across patient tissues and cancer cell-line models. COLGALT1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, COLGALT1 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, COLGALT1 protein abundance shows 26,229 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where COLGALT1 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 COLGALT1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes COLGALT1 survival associations across molecular data types. COLGALT1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible COLGALT1 RNA expression–survival associations across cancer types. High COLGALT1 expression shows unfavorable associations in ACC, MESO, KIRC, BLCA, KIRP and LIHC. The ACC 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 ACC as the clearest survival context for COLGALT1 RNA expression.
This table summarizes COLGALT1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for COLGALT1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. COLGALT1 shows higher tumor expression in HNSC, KIRC, KIRP, COAD, STAD and LIHC. The HNSC box plot shows higher COLGALT1 RNA expression in tumor versus normal tissue (log2 FC = +2.186, t-test p < 0.001).
This table shows molecular features associated with COLGALT1 in patient tissues and cancer cell lines. In patient samples, COLGALT1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, COLGALT1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BONE and LARGE_INTESTINE.
