Q-omics provides the consensus-scored ECM2 profile across patient tissues and cancer cell-line models. ECM2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ECM2 is differentially expressed in 14, with the highest sampling consensus in KICH. Additionally, ECM2 protein abundance shows 22,307 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, KICH, and GBM as cancer lineages where ECM2 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 ECM2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ECM2 survival associations across molecular data types. ECM2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (11) 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 ECM2 RNA expression–survival associations across cancer types. High ECM2 expression shows unfavorable associations in ACC, LGG, UVM and KIRP, but favorable associations in LUAD and KIRC. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify ACC as the clearest survival context for ECM2 RNA expression.
This table summarizes ECM2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ECM2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ECM2 shows lower tumor expression in KICH, THCA, KIRP, LUSC, LUAD and BLCA. The KICH box plot shows higher ECM2 RNA expression in normal versus tumor tissue (log2 FC = −2.457, t-test p < 0.001).
This table shows molecular features associated with ECM2 in patient tissues and cancer cell lines. In patient samples, ECM2 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, ECM2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and CNS.
