Q-omics provides the consensus-scored SCNN1G profile across patient tissues and cancer cell-line models. SCNN1G expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, SCNN1G is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, SCNN1G RNA expression shows 14,588 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight SKCM, KIRC, and THYM as cancer lineages where SCNN1G 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 SCNN1G — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SCNN1G survival associations across molecular data types. SCNN1G RNA expression shows survival associations in the most cancer types (27), followed by mutation status (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SCNN1G RNA expression–survival associations across cancer types. High SCNN1G expression shows unfavorable associations in SKCM, KIRC, LGG and UVM, but favorable associations in BLCA and LUAD. The SKCM 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 SKCM as the clearest survival context for SCNN1G RNA expression.
This table summarizes SCNN1G tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SCNN1G. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SCNN1G shows lower tumor expression in KIRC, KICH, KIRP, COAD, LUSC and LUAD. The KIRC box plot shows higher SCNN1G RNA expression in normal versus tumor tissue (log2 FC = −6.225, t-test p < 0.001).
This table shows molecular features associated with SCNN1G in patient tissues and cancer cell lines. In patient samples, SCNN1G shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, SCNN1G 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 CNS and BONE.
