Q-omics provides the consensus-scored ECM1 profile across patient tissues and cancer cell-line models. ECM1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ECM1 is differentially expressed in 14, with the highest sampling consensus in THCA. Additionally, ECM1 protein abundance shows 24,634 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight UVM, THCA, and PDAC as cancer lineages where ECM1 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 ECM1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ECM1 survival associations across molecular data types. ECM1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ECM1 RNA expression–survival associations across cancer types. High ECM1 expression shows unfavorable associations in UVM, BLCA, MESO and LGG, 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 < 0.001). Together, the overview and detailed table identify UVM as the clearest survival context for ECM1 RNA expression.
This table summarizes ECM1 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 HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ECM1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ECM1 shows lower tumor expression in LIHC and KICH and higher tumor expression in THCA, COAD, LUAD and BRCA. The THCA box plot shows higher ECM1 RNA expression in tumor versus normal tissue (log2 FC = +2.977, t-test p < 0.001).
This table shows molecular features associated with ECM1 in patient tissues and cancer cell lines. In patient samples, ECM1 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, ECM1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BONE.
