solute carrier family 9 member B1Genealiases: NHA1 · NHEDC1
Q-omics provides the consensus-scored SLC9B1 profile across patient tissues and cancer cell-line models. SLC9B1 expression is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in PAAD. Among the 18 cancer types available for tumor–normal comparison, SLC9B1 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, SLC9B1 RNA expression shows 19,962 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight PAAD, KICH, and UVM as cancer lineages where SLC9B1 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 SLC9B1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SLC9B1 survival associations across molecular data types. SLC9B1 RNA expression shows survival associations in the most cancer types (30), followed by mutation status (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SLC9B1 RNA expression–survival associations across cancer types. High SLC9B1 expression shows unfavorable associations in LUSC, but favorable associations in PAAD, ESCA, LGG, THYM and ACC. The PAAD 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 PAAD as the clearest survival context for SLC9B1 RNA expression.
This table summarizes SLC9B1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for SLC9B1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SLC9B1 shows lower tumor expression in KICH, THCA, KIRC and KIRP and higher tumor expression in HNSC and COAD. The KICH box plot shows higher SLC9B1 RNA expression in normal versus tumor tissue (log2 FC = −0.691, t-test p < 0.001).
This table shows molecular features associated with SLC9B1 in patient tissues and cancer cell lines. In patient samples, SLC9B1 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, SLC9B1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BONE.
