Q-omics provides the consensus-scored MMP23B profile across patient tissues and cancer cell-line models. MMP23B expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, MMP23B is differentially expressed in 8, with the highest sampling consensus in HNSC. Additionally, MMP23B protein abundance shows 15,052 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, HNSC, and LSCC as cancer lineages where MMP23B 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 MMP23B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MMP23B survival associations across molecular data types. MMP23B RNA expression shows survival associations in the most cancer types (23), followed by mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MMP23B RNA expression–survival associations across cancer types. High MMP23B expression shows unfavorable associations in ACC, KIRC, KIRP, LUSC and CESC, but favorable associations in SKCM. 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 MMP23B RNA expression.
This table summarizes MMP23B tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for MMP23B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MMP23B shows lower tumor expression in UCEC, BLCA, LUSC, LUAD and KICH and higher tumor expression in HNSC. The HNSC box plot shows higher MMP23B RNA expression in tumor versus normal tissue (log2 FC = +0.083, t-test p < 0.001).
This table shows molecular features associated with MMP23B in patient tissues and cancer cell lines. In patient samples, MMP23B shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, MMP23B 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 BLOOD_Leukemia.
