Q-omics provides the consensus-scored SEC23IP profile across patient tissues and cancer cell-line models. SEC23IP expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SEC23IP is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, SEC23IP protein abundance shows 31,888 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where SEC23IP 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 SEC23IP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SEC23IP survival associations across molecular data types. SEC23IP RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SEC23IP RNA expression–survival associations across cancer types. High SEC23IP expression shows unfavorable associations in ACC, CESC, UVM, PAAD and HNSC, but favorable associations in KIRC. 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 SEC23IP RNA expression.
This table summarizes SEC23IP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 11. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SEC23IP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SEC23IP shows lower tumor expression in THCA and higher tumor expression in HNSC, LIHC, LUAD, STAD and BRCA. The HNSC box plot shows higher SEC23IP RNA expression in tumor versus normal tissue (log2 FC = +0.413, t-test p < 0.001).
This table shows molecular features associated with SEC23IP in patient tissues and cancer cell lines. In patient samples, SEC23IP shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, SEC23IP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
