solute carrier family 9 member A1Genealiases: APNH · LIKNS · NHE-1 · NHE1 · PPP1R143
Q-omics provides the consensus-scored SLC9A1 profile across patient tissues and cancer cell-line models. SLC9A1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, SLC9A1 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, SLC9A1 protein abundance shows 23,830 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight MESO, HNSC, and GBM as cancer lineages where SLC9A1 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 SLC9A1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SLC9A1 survival associations across molecular data types. SLC9A1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (7) 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 SLC9A1 RNA expression–survival associations across cancer types. High SLC9A1 expression shows unfavorable associations in MESO, LGG, OV, ACC and LAML, but favorable associations in SCLC. The MESO 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 MESO as the clearest survival context for SLC9A1 RNA expression.
This table summarizes SLC9A1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SLC9A1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SLC9A1 shows lower tumor expression in COAD, KIRC and READ and higher tumor expression in HNSC, BRCA and PAAD. The HNSC box plot shows higher SLC9A1 RNA expression in tumor versus normal tissue (log2 FC = +1.048, t-test p < 0.001).
This table shows molecular features associated with SLC9A1 in patient tissues and cancer cell lines. In patient samples, SLC9A1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, SLC9A1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
