Q-omics provides the consensus-scored PAX2 profile across patient tissues and cancer cell-line models. PAX2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, PAX2 is differentially expressed in 9, with the highest sampling consensus in KICH. Additionally, PAX2 RNA expression shows 11,348 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight SCLC, KICH, and KIRP as cancer lineages where PAX2 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 PAX2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PAX2 survival associations across molecular data types. PAX2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PAX2 RNA expression–survival associations across cancer types. High PAX2 expression shows unfavorable associations in COAD, ACC, UCEC and THCA, but favorable associations in SCLC and KICH. The SCLC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify SCLC as the clearest survival context for PAX2 RNA expression.
This table summarizes PAX2 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 3. The strongest signals are observed in KICH for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for PAX2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PAX2 shows lower tumor expression in KICH, KIRC and PRAD and higher tumor expression in BRCA, COAD and KIRP. The KICH box plot shows higher PAX2 RNA expression in normal versus tumor tissue (log2 FC = −4.552, t-test p < 0.001).
This table shows molecular features associated with PAX2 in patient tissues and cancer cell lines. In patient samples, PAX2 shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, PAX2 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 CNS.
