Q-omics provides the consensus-scored MMP2 profile across patient tissues and cancer cell-line models. MMP2 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, MMP2 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, MMP2 protein abundance shows 25,837 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, HNSC, and LSCC as cancer lineages where MMP2 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 MMP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MMP2 survival associations across molecular data types. MMP2 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3) 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 MMP2 RNA expression–survival associations across cancer types. High MMP2 expression shows unfavorable associations in UVM, LGG, STAD and MESO, but favorable associations in UCEC and DLBC. The UVM Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify UVM as the clearest survival context for MMP2 RNA expression.
This table summarizes MMP2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for MMP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MMP2 shows lower tumor expression in KIRP and KICH and higher tumor expression in HNSC, LUAD, STAD and LIHC. The HNSC box plot shows higher MMP2 RNA expression in tumor versus normal tissue (log2 FC = +4.074, t-test p < 0.001).
This table shows molecular features associated with MMP2 in patient tissues and cancer cell lines. In patient samples, MMP2 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, MMP2 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 BONE and LARGE_INTESTINE.
