Q-omics provides the consensus-scored GYPA profile across patient tissues and cancer cell-line models. GYPA expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, GYPA is differentially expressed in 5, with the highest sampling consensus in KIRP. Additionally, GYPA protein abundance shows 14,574 significant protein co-abundance associations, with the highest sampling consensus in CCRCC. Together, these results highlight KIRC, KIRP, and CCRCC as cancer lineages where GYPA 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 GYPA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GYPA survival associations across molecular data types. GYPA RNA expression shows survival associations in the most cancer types (20), followed by mutation status (5) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GYPA RNA expression–survival associations across cancer types. High GYPA expression shows unfavorable associations in ESCA and CHOL, but favorable associations in KIRC, KIRP, READ and ACC. The KIRC 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 KIRC as the clearest survival context for GYPA RNA expression.
This table summarizes GYPA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRP for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GYPA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GYPA shows lower tumor expression in KIRP, KICH, KIRC, LUSC and LUAD. The KIRP box plot shows higher GYPA RNA expression in normal versus tumor tissue (log2 FC = −1.002, t-test p < 0.001).
This table shows molecular features associated with GYPA in patient tissues and cancer cell lines. In patient samples, GYPA shows the broadest associations at the RNA and protein expression levels, with CCRCC recurring as the lineage with the largest associated feature set. In cancer cell lines, GYPA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
