Q-omics provides the consensus-scored GGCX profile across patient tissues and cancer cell-line models. GGCX expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, GGCX is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, GGCX protein abundance shows 30,293 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, HNSC, and GBM as cancer lineages where GGCX 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 GGCX — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GGCX survival associations across molecular data types. GGCX RNA expression shows survival associations in the most cancer types (28), followed by mutation status (6) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GGCX RNA expression–survival associations across cancer types. High GGCX expression shows unfavorable associations in UVM, LGG, MESO, CESC and ACC, but favorable associations in UCS. The UVM 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 UVM as the clearest survival context for GGCX RNA expression.
This table summarizes GGCX tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 9. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for GGCX. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GGCX shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, BLCA, LUAD and STAD. The HNSC box plot shows higher GGCX RNA expression in tumor versus normal tissue (log2 FC = +1.005, t-test p < 0.001).
This table shows molecular features associated with GGCX in patient tissues and cancer cell lines. In patient samples, GGCX 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, GGCX RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and UPPER_AERODIGESTIVE_TRACT.
