Q-omics provides the consensus-scored GPATCH2L profile across patient tissues and cancer cell-line models. GPATCH2L expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, GPATCH2L is differentially expressed in 7, with the highest sampling consensus in HNSC. Additionally, GPATCH2L RNA expression shows 21,607 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight MESO, HNSC, and ACC as cancer lineages where GPATCH2L 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 GPATCH2L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GPATCH2L survival associations across molecular data types. GPATCH2L RNA expression shows survival associations in the most cancer types (29), followed by mutation status (7) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GPATCH2L RNA expression–survival associations across cancer types. High GPATCH2L expression shows unfavorable associations in MESO, ACC, LUSC and LIHC, but favorable associations in KIRC and UCS. The MESO Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .003). Together, the overview and detailed table identify MESO as the clearest survival context for GPATCH2L RNA expression.
This table summarizes GPATCH2L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for GPATCH2L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GPATCH2L shows lower tumor expression in THCA, BRCA and UCEC and higher tumor expression in HNSC, LIHC and CHOL. The HNSC box plot shows higher GPATCH2L RNA expression in tumor versus normal tissue (log2 FC = +0.753, t-test p < 0.001).
This table shows molecular features associated with GPATCH2L in patient tissues and cancer cell lines. In patient samples, GPATCH2L shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, GPATCH2L RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in STOMACH and UPPER_AERODIGESTIVE_TRACT.
