Q-omics provides the consensus-scored SERINC2 profile across patient tissues and cancer cell-line models. SERINC2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, SERINC2 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, SERINC2 RNA expression shows 13,388 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UCEC, HNSC, and TGCT as cancer lineages where SERINC2 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 SERINC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERINC2 survival associations across molecular data types. SERINC2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERINC2 RNA expression–survival associations across cancer types. High SERINC2 expression shows unfavorable associations in MESO, LGG, SARC and CESC, but favorable associations in UCEC and ACC. The UCEC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify UCEC as the clearest survival context for SERINC2 RNA expression.
This table summarizes SERINC2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for SERINC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERINC2 shows higher tumor expression in HNSC, THCA, LUAD, LUSC, BLCA and UCEC. The HNSC box plot shows higher SERINC2 RNA expression in tumor versus normal tissue (log2 FC = +1.561, t-test p < 0.001).
This table shows molecular features associated with SERINC2 in patient tissues and cancer cell lines. In patient samples, SERINC2 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, SERINC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and LUNG_NSCLC_LUAD.
