Q-omics provides the consensus-scored PCDHGA10 profile across patient tissues and cancer cell-line models. PCDHGA10 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, PCDHGA10 is differentially expressed in 8, with the highest sampling consensus in KICH. Additionally, PCDHGA10 RNA expression shows 16,458 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight CESC, KICH, and THYM as cancer lineages where PCDHGA10 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 PCDHGA10 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PCDHGA10 survival associations across molecular data types. PCDHGA10 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (8) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PCDHGA10 RNA expression–survival associations across cancer types. High PCDHGA10 expression shows unfavorable associations in CESC, HNSC, LUSC, ACC and STAD, but favorable associations in KIRC. The CESC 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 CESC as the clearest survival context for PCDHGA10 RNA expression.
This table summarizes PCDHGA10 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 2. The strongest signals are observed in KICH for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for PCDHGA10. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PCDHGA10 shows lower tumor expression in KICH, LUSC, THCA and LUAD and higher tumor expression in STAD and KIRC. The KICH box plot shows higher PCDHGA10 RNA expression in normal versus tumor tissue (log2 FC = −0.468, t-test p < 0.001).
This table shows molecular features associated with PCDHGA10 in patient tissues and cancer cell lines. In patient samples, PCDHGA10 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, PCDHGA10 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 CNS and LARGE_INTESTINE.
