The rapid advancement of nanotechnology and its materials has provided powerful technical support for the innovation of modern pharmaceutical formulations, driving the progress of multifunctionality, targeting, and intelligence in drug delivery systems. Compared to traditional drug formulations, nanomedicines are capable of interacting with the body at multiple levels and dimensions, going beyond the action mechanism of active pharmaceutical ingredients alone. This enables more precise drug targeting, sustained release, and significantly enhanced therapeutic effects. In the realm of nanomedicine, the choice of carrier materials has diversified, encompassing inorganic materials such as metal nanoparticles and mesoporous silica, as well as organic materials like polymeric substances and biomacromolecules. Notably, endogenous biomacromolecules, with their exceptional biocompatibility and inherent therapeutic properties, have emerged as a highly attractive category of carrier materials in recent years.

Distinct from conventional polymeric materials, DNA, as an exceptionally precise molecular tool, follows the Franklin-Watson-Crick base pairing rules, making molecular recognition extremely accurate. This unique characteristic enables DNA to construct highly controllable, specific, and complex target structures with specific morphology and functionality through meticulous sequence design, offering unprecedented possibilities for the design of nanomedicine carriers [1,2]. DNA nanostructures can be broadly classified based on sequence characteristics into DNA tiles, DNA bricks (single-stranded DNA tiles), and DNA origami structures. In 1982, N. Seeman successfully designed a stable four-stranded DNA structure composed of a specific number of single-stranded DNAs based on base complementarity and named them DNA tiles [3]. In 2012, the concept of DNA bricks was introduced, where these bricks consist of 42-base single-stranded DNA sequences connected by sticky ends [4]. As DNA tiles and DNA bricks typically exhibit 2D planar structures, they are widely utilized for further assembly into highly ordered 2D and 3D superlattices [5].

In 2006, to achieve larger and more discrete nanostructures, Paul W. K. Rothemund proposed the DNA origami technique. This method involves mixing hundreds of short single-stranded DNAs, known as “staples,” with a long circular scaffold DNA strand, followed by slow annealing to self-assemble into pre-designed nanostructures. DNA origami allows scientists to exploit the unique properties of DNA molecules to precisely fold and construct complex nanostructures. A key advantage of this technique is its spatial addressability: since each staple strand has a unique sequence, it corresponds to a specific spatial position. This feature allows for precise control over the position and quantity of target molecules. By incorporating hairpin structures into the staple strands, scientists can “write” information on the DNA structure or even draw intricate shapes, such as the outline of the Western Hemisphere [6].

DNA origami, with its pre-designed nanoscale shapes and precise spatial addressability, has emerged as a powerful tool in the field of nanotechnology. It finds wide applications in various areas such as drug encapsulation and release [7,8], protein structure and function modulation [9], molecular imaging [10], Chip-based detection [11,12], and more. DNA nanostructures of different dimensionalities exhibit significant differences in stability, immunogenicity, manufacturability, and cost when employed as drug delivery carriers. Generally, 3D DNA nanostructures possess superior spatial structural integrity and enhanced resistance to nuclease degradation in biological fluids, thereby demonstrating better stability. However, their construction involves complex procedures, relying on precise design and multistrand assembly, which poses challenges for scale-up manufacturing and results in relatively higher costs. In contrast, 2D structures such as DNA origami sheets strike a balance between structural stability and synthetic complexity, allowing modular designs that moderately facilitate scale-up. 1D structures, including DNA strands or nanowires, are simple to synthesize and cost-effective, enabling facile scale-up; however, they are prone to rapid degradation in vivo, which limits their stability and drug delivery efficiency.

Moreover, DNA nanostructures of different dimensionalities differ markedly in their immunostimulatory potential. Unmodified DNA nanostructures containing a high density of exposed CpG motifs may trigger immune responses via Toll-like receptor 9 (TLR9) pathways, with such immunostimulation being particularly prominent in 3D structures due to their higher spatial density [13]. Nevertheless, the terminal sequences of three-dimensional DNA nanostructures are often encapsulated within the internal spatial framework, reducing the exposure of CpG motifs and consequently lowering recognition and clearance by the immune system. By comparison, terminal sequences in 2D structures are partially exposed, resulting in a moderate degree of immune activation, while 1D DNA structures exhibit the greatest terminal exposure and are thus more readily recognized and responded to by the immune system.

While DNA, as a highly flexible molecular tool, can be engineered to create 2D or 3D geometric shapes, complex scaffolds, and more intricate specific forms, research has shown that DNA nanostructures of varying sizes, densities, and aspect ratios exhibit distinct behaviors in terms of cellular uptake and resistance to degradation [14,15]. However, there is still no widespread consensus on the selection of optimal structural forms for different applications [16]. Therefore, this review aims to explore the application of DNA nanostructures of various dimensions in different diseases (Fig. 1). Based on their structural characteristics and in vivo behaviors, we provide suggestions for structure optimization in the context of various diseases, with the goal of offering both theoretical insights and practical guidance for the selection of optimal shapes.