Q-omics provides the consensus-scored NEXMIF profile across patient tissues and cancer cell-line models. NEXMIF expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, NEXMIF is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, NEXMIF RNA expression shows 18,040 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight CESC, KIRC, and UVM as cancer lineages where NEXMIF 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 NEXMIF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NEXMIF survival associations across molecular data types. NEXMIF RNA expression shows survival associations in the most cancer types (28), followed by mutation status (13) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NEXMIF RNA expression–survival associations across cancer types. High NEXMIF expression shows unfavorable associations in BLCA, but favorable associations in CESC, OV, HNSC, PAAD and ACC. The CESC 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 CESC as the clearest survival context for NEXMIF RNA expression.
This table summarizes NEXMIF tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NEXMIF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NEXMIF shows lower tumor expression in KIRC, COAD, KICH, THCA, BLCA and STAD. The KIRC box plot shows higher NEXMIF RNA expression in normal versus tumor tissue (log2 FC = −1.319, t-test p < 0.001).
This table shows molecular features associated with NEXMIF in patient tissues and cancer cell lines. In patient samples, NEXMIF shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, NEXMIF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in OESOPHAGUS and BONE.
