Q-omics provides the consensus-scored GCNA profile across patient tissues and cancer cell-line models. GCNA expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, GCNA is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, GCNA RNA expression shows 18,351 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight BLCA, COAD, and UVM as cancer lineages where GCNA 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 GCNA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GCNA survival associations across molecular data types. GCNA RNA expression shows survival associations in the most cancer types (23), 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 GCNA RNA expression–survival associations across cancer types. High GCNA expression shows unfavorable associations in UVM, KIRC, KIRP, ACC and COAD, but favorable associations in BLCA. The BLCA 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 BLCA as the clearest survival context for GCNA RNA expression.
This table summarizes GCNA 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 GCNA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GCNA shows lower tumor expression in KICH and BRCA and higher tumor expression in COAD, KIRC, LIHC and CHOL. The COAD box plot shows higher GCNA RNA expression in tumor versus normal tissue (log2 FC = +0.411, t-test p < 0.001).
This table shows molecular features associated with GCNA in patient tissues and cancer cell lines. In patient samples, GCNA shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, GCNA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and SOFT_TISSUE.
