Q-omics provides the consensus-scored NMI profile across patient tissues and cancer cell-line models. NMI expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, NMI is differentially expressed in 16, with the highest sampling consensus in KIRC. Additionally, NMI protein abundance shows 21,132 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SKCM, KIRC, and GBM as cancer lineages where NMI 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 NMI — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NMI survival associations across molecular data types. NMI RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) 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 NMI RNA expression–survival associations across cancer types. High NMI expression shows unfavorable associations in KIRP, ACC, LGG and LUAD, but favorable associations in SKCM and KIRC. The SKCM 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 SKCM as the clearest survival context for NMI RNA expression.
This table summarizes NMI tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NMI. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NMI shows lower tumor expression in KICH and higher tumor expression in KIRC, HNSC, STAD, KIRP and BLCA. The KIRC box plot shows higher NMI RNA expression in tumor versus normal tissue (log2 FC = +1.259, t-test p < 0.001).
This table shows molecular features associated with NMI in patient tissues and cancer cell lines. In patient samples, NMI 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, NMI RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and SOFT_TISSUE.
