Q-omics provides the consensus-scored EXOG profile across patient tissues and cancer cell-line models. EXOG expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, EXOG is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, EXOG RNA expression shows 21,064 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, HNSC, and ACC as cancer lineages where EXOG 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 EXOG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EXOG survival associations across molecular data types. EXOG RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EXOG RNA expression–survival associations across cancer types. High EXOG expression shows unfavorable associations in LIHC, ACC, KICH, LGG and MESO, but favorable associations in PAAD. The LIHC 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 LIHC as the clearest survival context for EXOG RNA expression.
This table summarizes EXOG tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EXOG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EXOG shows lower tumor expression in KICH, THCA and LUSC and higher tumor expression in HNSC, LIHC and COAD. The HNSC box plot shows higher EXOG RNA expression in tumor versus normal tissue (log2 FC = +0.549, t-test p < 0.001).
This table shows molecular features associated with EXOG in patient tissues and cancer cell lines. In patient samples, EXOG shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, EXOG 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 KIDNEY and BLOOD_Leukemia.
