Q-omics provides the consensus-scored SCGB1C2 profile across patient tissues and cancer cell-line models. SCGB1C2 expression is associated with patient survival in 13 of 34 cancer types, with the highest sampling consensus in LUSC. Among the 18 cancer types available for tumor–normal comparison, SCGB1C2 is differentially expressed in 3, with the highest sampling consensus in KIRC. Additionally, SCGB1C2 RNA expression shows 5,969 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight LUSC, KIRC, and TGCT as cancer lineages where SCGB1C2 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 SCGB1C2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SCGB1C2 survival associations across molecular data types. SCGB1C2 RNA expression shows survival associations in the most cancer types (13). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SCGB1C2 RNA expression–survival associations across cancer types. High SCGB1C2 expression shows unfavorable associations in LUSC, STAD, PAAD, DLBC, ACC and UVM. The LUSC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .014). Together, the overview and detailed table identify LUSC as the clearest survival context for SCGB1C2 RNA expression.
This table summarizes SCGB1C2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 3. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for SCGB1C2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SCGB1C2 shows lower tumor expression in KIRC, KICH and KIRP. The KIRC box plot shows higher SCGB1C2 RNA expression in normal versus tumor tissue (log2 FC = −0.340, t-test p < 0.001).
This table shows molecular features associated with SCGB1C2 in patient tissues and cancer cell lines. In patient samples, SCGB1C2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, SCGB1C2 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.
