Q-omics provides the consensus-scored ECSIT profile across patient tissues and cancer cell-line models. ECSIT expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, ECSIT is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, ECSIT protein abundance shows 21,664 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KICH, THCA, and LSCC as cancer lineages where ECSIT 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 ECSIT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ECSIT survival associations across molecular data types. ECSIT RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ECSIT RNA expression–survival associations across cancer types. High ECSIT expression shows unfavorable associations in KICH and ACC, but favorable associations in KIRC, UVM, LGG and HNSC. The KICH 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 KICH as the clearest survival context for ECSIT RNA expression.
This table summarizes ECSIT 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 THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ECSIT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ECSIT shows lower tumor expression in THCA, UCEC, LUAD and BRCA and higher tumor expression in LIHC and LUSC. The THCA box plot shows higher ECSIT RNA expression in normal versus tumor tissue (log2 FC = −0.908, t-test p < 0.001).
This table shows molecular features associated with ECSIT in patient tissues and cancer cell lines. In patient samples, ECSIT 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, ECSIT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in LIVER and BLOOD_Lymphoma.
