Q-omics provides the consensus-scored SLC52A2 profile across patient tissues and cancer cell-line models. SLC52A2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, SLC52A2 is differentially expressed in 16, with the highest sampling consensus in COAD. Additionally, SLC52A2 RNA expression shows 18,782 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, COAD, and ACC as cancer lineages where SLC52A2 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 SLC52A2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SLC52A2 survival associations across molecular data types. SLC52A2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SLC52A2 RNA expression–survival associations across cancer types. High SLC52A2 expression shows unfavorable associations in UVM, KIRC, KIRP, CESC, ACC and LIHC. The UVM 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 UVM as the clearest survival context for SLC52A2 RNA expression.
This table summarizes SLC52A2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SLC52A2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SLC52A2 shows higher tumor expression in COAD, HNSC, KIRC, BLCA, KIRP and STAD. The COAD box plot shows higher SLC52A2 RNA expression in tumor versus normal tissue (log2 FC = +2.240, t-test p < 0.001).
This table shows molecular features associated with SLC52A2 in patient tissues and cancer cell lines. In patient samples, SLC52A2 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, SLC52A2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and SKIN.
