Q-omics provides the consensus-scored SEC16A profile across patient tissues and cancer cell-line models. SEC16A expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SEC16A is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, SEC16A RNA expression shows 20,649 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where SEC16A 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 SEC16A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SEC16A survival associations across molecular data types. SEC16A RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) 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 SEC16A RNA expression–survival associations across cancer types. High SEC16A expression shows unfavorable associations in ACC, MESO and BLCA, but favorable associations in SCLC, KIRC and PAAD. The ACC 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 ACC as the clearest survival context for SEC16A RNA expression.
This table summarizes SEC16A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SEC16A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SEC16A shows lower tumor expression in THCA and higher tumor expression in HNSC, BRCA, LIHC, STAD and CHOL. The HNSC box plot shows higher SEC16A RNA expression in tumor versus normal tissue (log2 FC = +0.748, t-test p < 0.001).
This table shows molecular features associated with SEC16A in patient tissues and cancer cell lines. In patient samples, SEC16A 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, SEC16A 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 BREAST.
