four and a half LIM domains 2Genealiases: AAG11 · DRAL · FHL-2 · SLIM-3 · SLIM3
Q-omics provides the consensus-scored FHL2 profile across patient tissues and cancer cell-line models. FHL2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, FHL2 is differentially expressed in 13, with the highest sampling consensus in LUAD. Additionally, FHL2 protein abundance shows 22,879 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight HNSC, LUAD, and PDAC as cancer lineages where FHL2 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 FHL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FHL2 survival associations across molecular data types. FHL2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) 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 FHL2 RNA expression–survival associations across cancer types. High FHL2 expression shows unfavorable associations in HNSC, ACC, LUSC, THCA, LUAD and CESC. The HNSC 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 HNSC as the clearest survival context for FHL2 RNA expression.
This table summarizes FHL2 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 LUAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for FHL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FHL2 shows lower tumor expression in THCA and KICH and higher tumor expression in LUAD, COAD, HNSC and LUSC. The LUAD box plot shows higher FHL2 RNA expression in tumor versus normal tissue (log2 FC = +3.291, t-test p < 0.001).
This table shows molecular features associated with FHL2 in patient tissues and cancer cell lines. In patient samples, FHL2 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, FHL2 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 BONE and OVARY.
