Q-omics provides the consensus-scored FHL3 profile across patient tissues and cancer cell-line models. FHL3 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, FHL3 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, FHL3 protein abundance shows 29,160 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight LIHC, KIRC, and PDAC as cancer lineages where FHL3 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 FHL3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FHL3 survival associations across molecular data types. FHL3 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (1) 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 FHL3 RNA expression–survival associations across cancer types. High FHL3 expression shows unfavorable associations in LIHC, MESO, ACC, KIRP and LGG, but favorable associations in UVM. 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 FHL3 RNA expression.
This table summarizes FHL3 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 LUAD for protein.
This table ranks reproducible tumor–normal expression differences for FHL3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FHL3 shows lower tumor expression in KICH and BLCA and higher tumor expression in KIRC, THCA, LIHC and LUAD. The KIRC box plot shows higher FHL3 RNA expression in tumor versus normal tissue (log2 FC = +0.989, t-test p < 0.001).
This table shows molecular features associated with FHL3 in patient tissues and cancer cell lines. In patient samples, FHL3 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, FHL3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
