Q-omics provides the consensus-scored CNOT2 profile across patient tissues and cancer cell-line models. CNOT2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, CNOT2 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, CNOT2 RNA expression shows 20,223 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where CNOT2 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 CNOT2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CNOT2 survival associations across molecular data types. CNOT2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (8) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CNOT2 RNA expression–survival associations across cancer types. High CNOT2 expression shows unfavorable associations in ACC, LIHC, MESO and KICH, but favorable associations in SCLC and UCS. The ACC 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 ACC as the clearest survival context for CNOT2 RNA expression.
This table summarizes CNOT2 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 HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CNOT2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CNOT2 shows lower tumor expression in THCA and higher tumor expression in HNSC, BLCA, KIRC, STAD and LIHC. The HNSC box plot shows higher CNOT2 RNA expression in tumor versus normal tissue (log2 FC = +0.684, t-test p < 0.001).
This table shows molecular features associated with CNOT2 in patient tissues and cancer cell lines. In patient samples, CNOT2 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, CNOT2 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 OVARY.
