Q-omics provides the consensus-scored SEC61G profile across patient tissues and cancer cell-line models. SEC61G expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, SEC61G is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, SEC61G RNA expression shows 19,448 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight HNSC, and ACC as cancer lineages where SEC61G 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 SEC61G — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SEC61G survival associations across molecular data types. SEC61G RNA expression shows survival associations in the most cancer types (28), followed by mutation status (3) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SEC61G RNA expression–survival associations across cancer types. High SEC61G expression shows unfavorable associations in HNSC, KICH, UVM, ACC, LUAD and LIHC. The HNSC 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 HNSC as the clearest survival context for SEC61G RNA expression.
This table summarizes SEC61G 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 5. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SEC61G. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SEC61G shows higher tumor expression in HNSC, KIRC, BLCA, LUAD, KIRP and LIHC. The HNSC box plot shows higher SEC61G RNA expression in tumor versus normal tissue (log2 FC = +1.528, t-test p < 0.001).
This table shows molecular features associated with SEC61G in patient tissues and cancer cell lines. In patient samples, SEC61G shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, SEC61G RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and CNS.
