Q-omics provides the consensus-scored MEGF8 profile across patient tissues and cancer cell-line models. MEGF8 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, MEGF8 is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, MEGF8 RNA expression shows 20,119 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and LIHC as cancer lineages where MEGF8 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 MEGF8 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MEGF8 survival associations across molecular data types. MEGF8 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (9) 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 MEGF8 RNA expression–survival associations across cancer types. High MEGF8 expression shows unfavorable associations in ACC, LGG, LUAD and LIHC, but favorable associations in KIRC and PAAD. The ACC 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 ACC as the clearest survival context for MEGF8 RNA expression.
This table summarizes MEGF8 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 3. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MEGF8. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MEGF8 shows lower tumor expression in THCA, BRCA and BLCA and higher tumor expression in LIHC, CHOL and KICH. The LIHC box plot shows higher MEGF8 RNA expression in tumor versus normal tissue (log2 FC = +1.816, t-test p < 0.001).
This table shows molecular features associated with MEGF8 in patient tissues and cancer cell lines. In patient samples, MEGF8 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, MEGF8 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
