Q-omics provides the consensus-scored NECAB1 profile across patient tissues and cancer cell-line models. NECAB1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in OV. Among the 18 cancer types available for tumor–normal comparison, NECAB1 is differentially expressed in 15, with the highest sampling consensus in LUAD. Additionally, NECAB1 RNA expression shows 22,004 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight OV, LUAD, and GBM as cancer lineages where NECAB1 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 NECAB1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NECAB1 survival associations across molecular data types. NECAB1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) 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 NECAB1 RNA expression–survival associations across cancer types. High NECAB1 expression shows unfavorable associations in OV, BLCA, KICH and STAD, but favorable associations in KIRC and KIRP. The OV Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .005). Together, the overview and detailed table identify OV as the clearest survival context for NECAB1 RNA expression.
This table summarizes NECAB1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for NECAB1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NECAB1 shows lower tumor expression in LUAD, THCA, BLCA, KIRC, COAD and LUSC. The LUAD box plot shows higher NECAB1 RNA expression in normal versus tumor tissue (log2 FC = −3.264, t-test p < 0.001).
This table shows molecular features associated with NECAB1 in patient tissues and cancer cell lines. In patient samples, NECAB1 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, NECAB1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LUNG_NSCLC_LUAD.
