Q-omics provides the consensus-scored SREBF2-AS1 profile across patient tissues and cancer cell-line models. SREBF2-AS1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, SREBF2-AS1 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, SREBF2-AS1 RNA expression shows 18,897 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, HNSC, and ACC as cancer lineages where SREBF2-AS1 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 SREBF2-AS1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SREBF2-AS1 survival associations across molecular data types. SREBF2-AS1 RNA expression shows survival associations in the most cancer types (26). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SREBF2-AS1 RNA expression–survival associations across cancer types. High SREBF2-AS1 expression shows unfavorable associations in LIHC, ACC, KICH and MESO, but favorable associations in LUSC and READ. The LIHC 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 LIHC as the clearest survival context for SREBF2-AS1 RNA expression.
This table summarizes SREBF2-AS1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for SREBF2-AS1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SREBF2-AS1 shows lower tumor expression in THCA and higher tumor expression in HNSC, LIHC, STAD, LUSC and KICH. The HNSC box plot shows higher SREBF2-AS1 RNA expression in tumor versus normal tissue (log2 FC = +0.685, t-test p < 0.001).
This table shows molecular features associated with SREBF2-AS1 in patient tissues and cancer cell lines. In patient samples, SREBF2-AS1 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set.
