Q-omics provides the consensus-scored SCNN1D profile across patient tissues and cancer cell-line models. SCNN1D expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, SCNN1D is differentially expressed in 9, with the highest sampling consensus in KIRC. Additionally, SCNN1D RNA expression shows 16,029 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight HNSC, KIRC, and UVM as cancer lineages where SCNN1D 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 SCNN1D — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SCNN1D survival associations across molecular data types. SCNN1D RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SCNN1D RNA expression–survival associations across cancer types. High SCNN1D expression shows unfavorable associations in KIRC, KICH and COAD, but favorable associations in HNSC, PAAD and SKCM. The HNSC 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 HNSC as the clearest survival context for SCNN1D RNA expression.
This table summarizes SCNN1D tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for SCNN1D. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SCNN1D shows lower tumor expression in KICH and higher tumor expression in KIRC, HNSC, LIHC, COAD and KIRP. The KIRC box plot shows higher SCNN1D RNA expression in tumor versus normal tissue (log2 FC = +0.802, t-test p < 0.001).
This table shows molecular features associated with SCNN1D in patient tissues and cancer cell lines. In patient samples, SCNN1D 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, SCNN1D RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BLOOD_Leukemia.
