Q-omics provides the consensus-scored PRXL2C profile across patient tissues and cancer cell-line models. PRXL2C expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PRXL2C is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, PRXL2C RNA expression shows 19,382 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight ACC, HNSC, and UVM as cancer lineages where PRXL2C 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 PRXL2C — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PRXL2C survival associations across molecular data types. PRXL2C RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PRXL2C RNA expression–survival associations across cancer types. High PRXL2C expression shows unfavorable associations in ACC, KIRP, UVM, LGG and LUSC, but favorable associations in KIRC. 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 PRXL2C RNA expression.
This table summarizes PRXL2C tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PRXL2C. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PRXL2C shows lower tumor expression in THCA, KICH, BLCA and LUAD and higher tumor expression in HNSC and KIRC. The HNSC box plot shows higher PRXL2C RNA expression in tumor versus normal tissue (log2 FC = +1.117, t-test p < 0.001).
This table shows molecular features associated with PRXL2C in patient tissues and cancer cell lines. In patient samples, PRXL2C shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, PRXL2C RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BREAST.
