Q-omics provides the consensus-scored AFMID profile across patient tissues and cancer cell-line models. AFMID expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, AFMID is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, AFMID RNA expression shows 19,352 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KICH as cancer lineages where AFMID 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 AFMID — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AFMID survival associations across molecular data types. AFMID RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) 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 AFMID RNA expression–survival associations across cancer types. High AFMID expression shows unfavorable associations in ACC, UCEC, LIHC and LGG, but favorable associations in BRCA 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 < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for AFMID RNA expression.
This table summarizes AFMID tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AFMID. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AFMID shows lower tumor expression in KICH, KIRC and KIRP and higher tumor expression in COAD, HNSC and LUAD. The KICH box plot shows higher AFMID RNA expression in normal versus tumor tissue (log2 FC = −4.151, t-test p < 0.001).
This table shows molecular features associated with AFMID in patient tissues and cancer cell lines. In patient samples, AFMID 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, AFMID RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BONE.
