Q-omics provides the consensus-scored SERHL profile across patient tissues and cancer cell-line models. SERHL expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SERHL is differentially expressed in 11, with the highest sampling consensus in LIHC. Additionally, SERHL RNA expression shows 16,939 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and LIHC as cancer lineages where SERHL 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 SERHL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERHL survival associations across molecular data types. SERHL RNA expression shows survival associations in the most cancer types (24). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERHL RNA expression–survival associations across cancer types. High SERHL expression shows unfavorable associations in ACC, KICH, LIHC and LAML, but favorable associations in UCEC and HNSC. 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 SERHL RNA expression.
This table summarizes SERHL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for SERHL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERHL shows lower tumor expression in KIRC, THCA and KICH and higher tumor expression in LIHC, CHOL and LUSC. The LIHC box plot shows higher SERHL RNA expression in tumor versus normal tissue (log2 FC = +0.523, t-test p < 0.001).
This table shows molecular features associated with SERHL in patient tissues and cancer cell lines. In patient samples, SERHL 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, SERHL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in BREAST.
