Q-omics provides the consensus-scored NET1 profile across patient tissues and cancer cell-line models. NET1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, NET1 is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, NET1 RNA expression shows 19,701 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, KICH, and ACC as cancer lineages where NET1 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 NET1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NET1 survival associations across molecular data types. NET1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NET1 RNA expression–survival associations across cancer types. High NET1 expression shows unfavorable associations in LIHC, ACC and PAAD, but favorable associations in KIRC, LGG and UCS. 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 NET1 RNA expression.
This table summarizes NET1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 1. The strongest signals are observed in KICH for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for NET1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NET1 shows lower tumor expression in KICH and KIRP and higher tumor expression in LUAD, LUSC, BRCA and STAD. The KICH box plot shows higher NET1 RNA expression in normal versus tumor tissue (log2 FC = −3.038, t-test p < 0.001).
This table shows molecular features associated with NET1 in patient tissues and cancer cell lines. In patient samples, NET1 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, NET1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
