Q-omics provides the consensus-scored NEO1 profile across patient tissues and cancer cell-line models. NEO1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, NEO1 is differentially expressed in 11, with the highest sampling consensus in COAD. Additionally, NEO1 protein abundance shows 19,337 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, COAD, and GBM as cancer lineages where NEO1 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 NEO1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NEO1 survival associations across molecular data types. NEO1 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (7) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NEO1 RNA expression–survival associations across cancer types. High NEO1 expression shows unfavorable associations in HNSC, LGG and CHOL, but favorable associations in KIRC, UVM and COAD. The KIRC 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 KIRC as the clearest survival context for NEO1 RNA expression.
This table summarizes NEO1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 5. The strongest signals are observed in COAD for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for NEO1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NEO1 shows lower tumor expression in COAD, THCA, LUAD and READ and higher tumor expression in CHOL and LIHC. The COAD box plot shows higher NEO1 RNA expression in normal versus tumor tissue (log2 FC = −1.013, t-test p < 0.001).
This table shows molecular features associated with NEO1 in patient tissues and cancer cell lines. In patient samples, NEO1 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, NEO1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
