Q-omics provides the consensus-scored GYG1 profile across patient tissues and cancer cell-line models. GYG1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, GYG1 is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, GYG1 protein abundance shows 21,139 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, and GBM as cancer lineages where GYG1 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 GYG1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GYG1 survival associations across molecular data types. GYG1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (2) 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 GYG1 RNA expression–survival associations across cancer types. High GYG1 expression shows unfavorable associations in LIHC, ESCA, STAD, LAML and KICH, but favorable associations in THCA. The LIHC 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 LIHC as the clearest survival context for GYG1 RNA expression.
This table summarizes GYG1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 6. The strongest signals are observed in LIHC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for GYG1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GYG1 shows lower tumor expression in BLCA, UCEC and PRAD and higher tumor expression in LIHC, CHOL and BRCA. The LIHC box plot shows higher GYG1 RNA expression in tumor versus normal tissue (log2 FC = +1.212, t-test p < 0.001).
This table shows molecular features associated with GYG1 in patient tissues and cancer cell lines. In patient samples, GYG1 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, GYG1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BLOOD_Leukemia.
