Q-omics provides the consensus-scored GPATCH4 profile across patient tissues and cancer cell-line models. GPATCH4 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, GPATCH4 is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, GPATCH4 protein abundance shows 27,779 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, COAD, and GBM as cancer lineages where GPATCH4 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 GPATCH4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GPATCH4 survival associations across molecular data types. GPATCH4 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (1) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GPATCH4 RNA expression–survival associations across cancer types. High GPATCH4 expression shows unfavorable associations in ACC, KIRP, LIHC, UVM, MESO and KICH. The ACC 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 ACC as the clearest survival context for GPATCH4 RNA expression.
This table summarizes GPATCH4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for GPATCH4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GPATCH4 shows higher tumor expression in COAD, HNSC, STAD, LIHC, LUAD and LUSC. The COAD box plot shows higher GPATCH4 RNA expression in tumor versus normal tissue (log2 FC = +1.332, t-test p < 0.001).
This table shows molecular features associated with GPATCH4 in patient tissues and cancer cell lines. In patient samples, GPATCH4 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, GPATCH4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and SOFT_TISSUE.
