Q-omics provides the consensus-scored FAT4 profile across patient tissues and cancer cell-line models. FAT4 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, FAT4 is differentially expressed in 13, with the highest sampling consensus in THCA. Additionally, FAT4 RNA expression shows 24,473 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, THCA, and LSCC as cancer lineages where FAT4 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 FAT4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FAT4 survival associations across molecular data types. FAT4 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (12) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FAT4 RNA expression–survival associations across cancer types. High FAT4 expression shows unfavorable associations in MESO, BLCA and STAD, but favorable associations in KIRC, HNSC and LIHC. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for FAT4 RNA expression.
This table summarizes FAT4 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 4. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for FAT4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FAT4 shows lower tumor expression in THCA, KICH, BLCA, LUAD, LUSC and UCEC. The THCA box plot shows higher FAT4 RNA expression in normal versus tumor tissue (log2 FC = −1.887, t-test p < 0.001).
This table shows molecular features associated with FAT4 in patient tissues and cancer cell lines. In patient samples, FAT4 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, FAT4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BONE and CNS.
